# YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their
application in the agricultural domain

Mujadded Al Rabbani Alif* and Muhammad Hussain
Department of Computer Science, Huddersfield University, Queensgate,
Huddersfield HD1 3DH, UK; *Correspondence<EMAIL_ADDRESS>
###### Abstract

This survey investigates the transformative potential of various YOLO
variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of
agricultural advancements. The primary objective is to elucidate how these
cutting-edge object detection models can re-energise and optimize diverse
aspects of agriculture, ranging from crop monitoring to livestock management.
It aims to achieve key objectives, including the identification of
contemporary challenges in agriculture, a detailed assessment of YOLO’s
incremental advancements, and an exploration of its specific applications in
agriculture. This is one of the first surveys to include the latest YOLOv10,
offering a fresh perspective on its implications for precision farming and
sustainable agricultural practices in the era of Artificial Intelligence and
automation. Further, the survey undertakes a critical analysis of YOLO’s
performance, synthesizes existing research, and projects future trends. By
scrutinizing the unique capabilities packed in YOLO variants and their real-
world applications, this survey provides valuable insights into the evolving
relationship between YOLO variants and agriculture. The findings contribute
towards a nuanced understanding of the potential for precision farming and
sustainable agricultural practices, marking a significant step forward in the
integration of advanced object detection technologies within the agricultural
sector.

_K_ eywords Precision Farming; Automation; Computer Vision; YOLO; Object
Detection; Agricultural Applications ; Real-Time Image processing; Deep
Learning in Agriculture; Convolutional Neural Networks(CNN); YOLO version
comparison; Automated Crop monitoring

##  1 Introduction

In recent years, the intersection of computer vision and agriculture has
experienced remarkable strides, unlocking a transformative era in precision
farming and agricultural management [1]. Among the key technologies driving
this paradigm shift is the evolution of the You Only Look Once (YOLO)
algorithm, a family of object detectors that have manifested exceptional
efficiency and accuracy. This review aims to delve into the mainstream YOLO
variants, starting with YOLOv1 and continuing with the latest advancements in
the form of YOLOv10. In particular, this exploration seeks to unravel the
emerging potential of YOLO variants in revolutionising agricultural practices
and fostering sustainable advancements. The YOLO family of object detectors,
introduced by Joseph Redmon in 2015 with YOLOv1, marked a watershed moment in
the object detection catalogue of architectures. YOLO’s distinctive
characteristic lies in its ability to perform real-time object detection by
dividing the input image into a grid matrix and predicting bounding boxes and
class probabilities simultaneously [2]. This shift from conventional two-stage
methodologies significantly enhanced speed and maintained competitive
accuracy, setting the stage for subsequent YOLO iterations. As YOLO progressed
through its reforms, each variant addressed limitations in addition to
introducing novel techniques for refining edge-based performance. The
transition from YOLOv1 to YOLOv10 witnessed advancements in several fields,
including architectural design, training strategies, and optimisation
techniques. The later variants of YOLO are designed to tackle challenges such
as small object targets, occlusions, and improving performance across diverse
datasets. It is important to understand these complexities to fully appreciate
the potential applicability of YOLO variants in complex sub-domains such as
agriculture.

Agriculture, being a multifaceted domain, demands robust and highly efficient
tools for monitoring and management of crops, livestock, and environmental
conditions [3]. The integration of YOLO variants in agricultural applications
is promising for revolutionising tasks such as crop monitoring [4], disease
detection [5], yield estimation [6], and livestock management. The real-time
capacity offered by YOLO, coupled with its accuracy and adaptability, make
YOLO variants an attractive solution for addressing the evolving challenges in
modern agriculture. Given the rising demands within the agricultural sector,
computer vision stands out as a transformative force for several reasons:

  * •

Scale and Precision: Automation, enabled by computer vision, accelerates
large-scale and precise operations. Computer vision algorithms can facilitate
the rapid and accurate analysis of visual data, allowing for the monitoring of
vast agricultural landscapes with unprecedented levels of speed and precision.

  * •

Efficiency and Resource Optimisation: The integration of computer vision can
improve the efficiency of automated processes. Advanced image recognition and
on-device analysis can enable the allocation of resources such as water,
fertilizers, and land. This not only maximises yield but also promotes
sustainable farming practices.

  * •

Real-time Response: Computer vision contributes to real-time monitoring and
analysis. Automated systems, fuelled by computer vision, can swiftly detect
and respond to emerging challenges, such as disease outbreaks or pest
invasions, ensuring a rapid and targeted intervention to alleviate potential
losses and maintain crop health.

  * •

Data-Driven Decision-Making: Computer vision augments automation by providing
a wealth of real-time visual data. This data-driven approach can enable
farmers and stakeholders to make informed decisions, improving overall farm
management and strategic planning.

In the subsequent sections of this review, we will navigate through key
advancements of each YOLO variant, elucidating enhancements introduced in each
variant. Subsequently, we will examine specific applications and the potential
impact of YOLO variants in agriculture, investigating how these variants can
contribute to sustainable farming practices and agricultural advancements. As
we navigate through this review, it becomes clear that the fusion of cutting-
edge computer vision technologies, represented by YOLO variants, with the
agricultural landscape holds the promise of driving a new era of precision
farming and resource optimization.

###  1.1 Survey Objective

This review endeavours to examine the transformative potential of YOLO
variants, spanning from YOLOv1 to the state-of-the-art YOLOv10, in the realm
of agricultural advancements. The overarching aim is to elucidate how these
state-of-the-art architectures belonging to the YOLO family can reshape and
optimise various facets of agriculture, ranging from crop monitoring to
livestock management. The primary focus will be on harnessing the unique
capabilities of YOLO variants to accommodate the dynamic challenges faced by
the agricultural sector. Specifically, this review aims to achieve the
following objectives:

Assessment of YOLO Evolution: We trace the fundamental advancements of each
YOLO variant, examining the architectural enhancements, algorithmic
refinements, and methodological innovations. By comprehensively understanding
the incremental evolution of YOLO variants, we can affirm the technological
strides that contribute to YOLO’s applicability in diverse agricultural
scenarios.

Exploration of YOLO Applications in Agriculture: We then explore the specific
applications of YOLO variants in agricultural sub-domains. This involves the
investigation of real-world use cases where YOLO has demonstrated high
efficacy, such as crop monitoring, livestock tracking, and detection of
anomalies in the agricultural landscape. By identifying these applications, we
can gauge the versatility of YOLO in tackling the multifaceted challenges of
modern agriculture.

Critical Analysis of YOLO’s Performance: A critical review of the performance
of YOLO variants in agricultural contexts is presented. This includes
assessing metrics such as detection accuracy, processing speed, and
adaptability to diverse agricultural environments. Through a nuanced analysis,
we are able to ascertain the strengths and limitations of YOLO in meeting the
specific demands of agriculture.

Synthesis of Existing Research: The survey aims to synthesize and analyse
existing research studies that have explored the intersection of YOLO variants
concerning the agricultural domain. By consolidating the findings of these
works, we unlock key insights and discern common trends, paving the way for a
comprehensive understanding of the current landscape.

Projection of Future Trends: We anticipate future trends crucial for
benchmarking the impact of YOLO variants in agriculture. This objective
involves extrapolating from current research and technological trajectories to
envisaging potential advancements, challenges, and emerging applications of
YOLO in the agricultural domain.

In brief, this review aspires to present a holistic manifestation of the
evolving relationship between YOLO variants and agriculture, unravelling the
layers of innovation that hold the promise of ushering in a new era of
precision farming and sustainable agricultural practices.

###  1.2 Organization of Paper

This review initiates by delineating agricultural challenges, followed by an
introduction to Convolutional Neural Networks (CNNs) to furnish readers with
foundational insights into the principles underpinning the YOLO framework.
Subsequently, a general introduction to object detection techniques is
presented to establish the necessary contextual background for the subsequent
discourse.

Furthermore, the review undertakes a comprehensive examination of the
evolutionary trajectories of YOLO architectures, systematically investigating
the modifications and enhancements introduced by each variant.

Subsequent to this examination, the review scrutinizes the application of YOLO
variants in sub-domains of agriculture, encompassing crop diseases, pest
infestations, resource optimisation, and precision farming. In the conclusive
segments, the findings are summarised in detail, culminating in a
comprehensive assessment of YOLO variants as a transformative solution for
agricultural domains.

##  2 Convolutional Neural Networks (CNN)

Deep Learning (DL) has emerged as a multi-domain innovation amid the
popularity of various Machine Learning (ML) techniques like Decision Trees
(DT), Support Vector Machines (SVM), KMeans, Multilayer Perceptron (MLP), and
Artificial Neural Networks (ANN). DL, a subset of ML and an Artificial
Intelligence (AI) component, has demonstrated remarkable success across
diverse domains. Its applications include biological data handling [7], speech
recognition [8], character recognition [9], micro-blogs [10], text
classification [11], unstructured text data mining with fault classification
[12], gene expression [13], bolt detection [14, 15], pallet damage detection
[16], automatic landslide detection [17], video processing, including caption
generation [18], intrusion detection [19] and stock market prediction [20].
However, these examples only scratch the surface of Deep Learning’s vast
potential.

In the context of this review, computer vision involves training machines to
comprehend and interpret visual content at a sophisticated level. This field
includes various subfields such as object detection [21], image restoration,
scene or object recognition, pose and motion estimation, object segmentation,
and video tracking, among others [22]. Unlike conventional image processing,
which requires manual feature extraction through the definition of feature
descriptors, deep learning architectures serve as automatic feature
extractors. This makes deep learning a lucrative alternative, enabling
researchers to overcome conventional image processing constraints and focus
more on improving application-specific performance.

Deep learning models encompass various techniques, including Recurrent Neural
Networks (RNNs) for sequential data processing and their architectural
variants, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
for memory and context preservation [23]. Convolutional Neural Networks (CNNs)
specialize in visual perception tasks involving image data, as other DL
algorithms like ANNs struggle with scaling inefficiencies when confronted with
high-dimensional input data, such as images.

The architectural footprint of Convolutional Neural Networks (CNNs) at an
abstract level consists of a set of convolution, pooling, and activation
functions that transform inputs through a staged process to reach the
appropriate output.

A fundamental component within convolutional blocks of a CNN is defining the
number of kernels/filters and their respective dimensions. These are crucial
for feature extraction, providing low-level spatial information to subsequent
layers for developing semantic relationships [24].

$q_{i}^{l}=f\left(b_{i}+\sum_{j=0}^{d-1}w_{i+j}x_{i+j}\right)$ (1)

$q_{ij}^{l}=f\left(b_{ij}+\sum_{k=0}^{d1-1}\sum_{l=0}^{d2-1}w_{(i+k)(j+l)}x_{(i+k)(j+l)}\right)$
(2)

Similar to ANNs, the weights ($w$) and spatial input ($x$) are multiplied via
the dot product. After introducing the bias term ($b$), a non-linear
activation function ($f$) is applied.

Equations (1) and (2) express the convolution operation for the input,
respectively. In these equations, $q_{i}^{l}$ represents the output of the
$i$-th neuron in layer $l$. For text input, the filter size is denoted as $d$,
while for visual input, $d_{1}$ and $d_{2}$ represent the filter width and
filter height.

The output post-convolving is subjected to pooling to extract prominent
features through aggregation, i.e., downsampling along the spatial dimensions.

Multifaceted aggregation, i.e., pooling frameworks, are available [25], such
as average pooling, sum pooling, and max pooling. For example, the
mathematical composition of the latter, i.e., the max pooling function, is
expressed by equation 3.

$q_{i}^{l}=\max(q_{(i-j)}^{(l-1)},q_{(i+j)}^{(l-1)})$ (3)

Rectified Linear Unit (ReLU) is the profoundly utilised activation function
within the convolutional blocks, as it is, in essence, a ’max-operation’,
making it computationally lightweight for its mathematical chemistry, as
expressed via (4) in comparison to the sigmoid and TanH expressed via (5) and
(6), respectively.

$f(x)=\max(0,x)$ (4)

$x\rightarrow\frac{1}{1+e^{-x}}$ (5)

$\tanh(x)=2\sigma(2x)-1$ (6)

The visual manifestation of an abstract CNN is presented in Figure 1. The key
components of CNNs can be labelled as a set of convolutional blocks consisting
of filters that can be optimised, followed by a defined number of fully
connected layers leading to the output [26].

Figure 1: The General structure of a CNN, highlighting convolutional layers,
pooling, and fully connected layers.

##  3 Object Detection

The development of effective object detectors presents several challenges for
researchers and practitioners. A primary concern is handling variations in
image resolutions and aspect ratios, which becomes more challenging when
target objects exhibit significant differences in spatial dimensions. Class
imbalance, especially in scenarios where obtaining an adequate number of
images for certain classes is difficult, can negatively impact the performance
of object detectors, resulting in biased predictions [27].

Another significant challenge lies in the computational complexity of object
detection architectures, demanding substantial computational resources in
terms of power, memory, and time [28, 29]. Figure 2 illustrates object
detection for single and multiple objects in an image, depicting detectors
with deep internal networks that require significant computational resources
for processing complex image datasets and extracting crucial features.

Figure 2: Single and multiple objects in an image: Classification,
Localization, Segmentation. Figure 3: Abstract architecture of single-stage
object detectors.

Object detection methods can be categorized into Two-stage object detectors
and single-stage detectors. The former proposes candidate regions in the
image, followed by classification and localization within those proposed
regions. Examples of two-stage detectors include RCNN (Region-based
Convolutional Neural Network) [30], Fast R-CNN [31], Faster R-CNN [32], and
FPN (Feature Pyramid Network) [33].

RCNN [30], introduced in 2014, utilized a selective search for candidate
region proposals. It then employed a CNN network for feature extraction,
followed by an SVM classifier for classification and localization. Although
accurate, RCNN was computationally inefficient due to its two-stage process.
Fast R-CNN [31] addressed efficiency concerns by introducing ROI pooling. This
approach used ROI pooling to extract fixed-size feature maps for each region
from the original feature maps, resulting in significant computational speed-
up. Faster R-CNN [32] improved upon Fast R-CNN by introducing the Region
Proposal Network (RPN), which directly generated region proposals from
convolutional feature maps, eliminating the need for a separate proposal
stage. Integration of RPN into Fast R-CNN enhanced both speed and accuracy.
FPN (Feature Pyramid Network) [33] enhanced two-stage detectors by addressing
the challenge of detecting targets at multiple scales. FPN generated a feature
pyramid by incorporating feature maps of varying resolutions from different
stages of the network, enabling the model to detect targets of different
scales effectively.

While two-stage detectors exhibit impressive accuracy, their high
computational demand limits their applications. Single-stage detectors aim to
detect objects in a single pass, eliminating the need for a separate region
proposal step, as shown in Figure 3. Notable single-stage detectors include
SSD (Single Shot Multibox Detector), YOLO variants (You Only Look Once),
RefineDet++, DSSD (Deconvolution Single Shot Detector), and RetinaNet. SSD
[34] utilizes multiple convolutional feature maps at different scales for
predicting bounding boxes and class probability scores. It effectively detects
objects of various sizes and shapes in a single forward pass. RefineDet++[35]
enhances the original RefineDet architecture by refining target proposals
iteratively through multiple stages. Improved feature fusion mechanisms and
refined target boundaries contribute to enhanced accuracy. DSSD (Deconvolution
Single Shot Detector) incorporates deconvolution layers to retain spatial
information lost during feature pooling. This aids in maintaining spatial
resolution, allowing DSSD to capture fine-grained details. RetinaNet [36]
addresses class imbalance with Focal Loss, assigning higher weights to hard,
misclassified samples, improving the architecture’s ability to handle class
imbalance and enhance detection performance.

Single-stage detectors offer advantages such as faster inference speed and a
lightweight footprint compared to two-stage detectors, making them suitable
for resource-constrained environments. YOLO has emerged as a strong competitor
among single-stage detectors, demonstrating impressive accuracy and real-time
inference capabilities due to its straightforward architecture. It has proven
to be effective in various real-world applications, showcasing its potential
for production purposes.

##  4 YOLO Architecture Background

This section delves into the foundational principles and architecture
underlying YOLO, detailing the unique advancements associated with each
iteration. The YOLO algorithm, introduced in 2015 by Joesph Redmon et al.
[37], stands for "You Only Look Once." This name reflects its distinctive
approach, examining the entire image just once to identify objects and their
positions. In contrast to conventional methods employing two-stage detection
processes, YOLO treats object detection as a regression problem [37]. In the
YOLO paradigm, a single convolutional neural network is employed to predict
bounding boxes and class probabilities for an entire image. This streamlined
approach differs from traditional methods with more intricate pipelines.

###  4.1 YOLOv1

The core concept of YOLOv1 entails superimposing a grid cell-sized "s x s"
onto an image. Whenever an object’s centre lands within a grid cell, that
specific cell is tasked with identifying the object, enabling other cells to
ignore its existence in situations where multiple instances occur. Regarding
object detection, every grid cell anticipates "B" bounding boxes, complete
with dimensions and confidence scores. The confidence score indicates the
probability of an object residing within the designated bounding box.
Mathematically, the confidence score is represented as Equation (7):

$\text{confidence score}=p(\text{object})\times\text{IoU}_{\text{truth pred}}$
(7)

In this context, $p(\text{object})$ denotes the likelihood of the object’s
presence (with a range between 0 and 1), and IoU${}_{\text{truth pred}}$
represents the intersection-over-union between the predicted bounding box and
the ground truth.

The primary goal is accurately identifying and localising objects using
bounding boxes. YOLO tackles challenges related to overlapping predicted
bounding boxes through a non-maximum suppression (NMS) mechanism, eliminating
those with an Intersection over Union (IoU) below a specified threshold. The
original YOLO architecture, built on Darknet, introduced two sub-variants: one
with 24 convolutional layers and another called ’Fast YOLO’ with nine layers.
Different penalties were assigned for bounding boxes containing objects and
those indicating the absence of an object. The overall loss function
incorporated coordinates, width, height, confidence score, and class
probability considerations.

In terms of performance, the simpler YOLO version achieved a mean average
precision (mAP) of 63.4% at 45 frames per second (FPS), while the Fast YOLO
variant reached 52.7% at 155 FPS. Despite surpassing some real-time detectors,
they fell short of state-of-the-art (SOTA) benchmarks at that particular time.
Nevertheless, limitations such as lower recall and localization errors spurred
further advancements in subsequent YOLO variants.

###  4.2 YOLOv2

Expanding on the achievements of YOLOv1, YOLOv2 brings forth notable
enhancements in its design. This version incorporates ideas from Network-In-
Network and VGG, opting for the Darknet-19 framework, consisting of 19
convolutional layers and 5 layers specifically designated for maximum pooling,
as detailed in Table 1. YOLOv2 employs a blend of pooling layers and 1 x 1
convolutions, enabling down-sampling within the network architecture.

Table 1: Darknet-19 framework Type | Filters | Size/Stride | Output
---|---|---|---
Convolutional | 32 | 3 x 3 | 224 x 224
Maxpool | 2 x 2/2 | 112 x 112 |
Convolutional | 64 | 3 x 3 | 112 x 112
Maxpool | 2 x 2/2 | 56 x 56 |
Convolutional | 128 | 3 x 3 | 56 x 56
Convolutional | 64 | 1 x 1 | 56 x 56
Convolutional | 128 | 3 x 3 | 56 x 56
Maxpool | 2 x 2/2 | 28 x 28 |
Convolutional | 256 | 3 x 3 | 28 x 28
Convolutional | 128 | 1 x 1 | 28 x 28
Convolutional | 256 | 3 x 3 | 28 x 28
Maxpool | 2 x 2/2 | 14 x 14 |
Convolutional | 512 | 3 x 3 | 14 x 14
Convolutional | 256 | 1 x 1 | 14 x 14
Convolutional | 512 | 3 x 3 | 14 x 14
Convolutional | 256 | 1 x 1 | 14 x 14
Convolutional | 512 | 3 x 3 | 14 x 14
Maxpool | 2 x 2/2 | 7 x 7 |
Convolutional | 1024 | 3 x 3 | 7 x 7
Convolutional | 512 | 1 x 1 | 7 x 7
Convolutional | 1024 | 3 x 3 | 7 x 7
Convolutional | 512 | 1 x 1 | 7 x 7
Convolutional | 1024 | 3 x 3 | 7 x 7
Convolutional | 1000 | 1 x 1 | 7 x 7
Avgpool | Global | 1000 |
Softmax |  |  |

An essential challenge in object detection lies in the limited availability of
labelled data, often confining methods to predetermined categories. YOLOv2
tackles this challenge by amalgamating the ImageNet and COCO datasets,
broadening its detection capabilities to encompass over 9418 object instances
[38]. For enhanced scalability, YOLOv2 adopts Word-Tree, a hierarchical
classification and detection approach adept at efficiently handling the
expanded array of categories.

Despite initial difficulties with small object detection, YOLOv2 marks
substantial improvements over its predecessor. It introduces diverse data
augmentation techniques and optimization strategies, yielding noteworthy
advancements:

  * •

YOLOv2 predicts object dimensions across a range of sizes, from 320 x 320 to
608 x 608, by discarding fully connected layers present in YOLOv1.

  * •

A 4% increase in mean average precision (mAP) is attained through a higher
resolution classifier. In contrast to V1, YOLOv2 undergoes training on 448 x
448 images for classification before detection, enhancing the accuracy of
bounding box predictions.

  * •

The integration of Batch Normalization addresses inconsistencies in input
distribution during training, resulting in an approximate 2% mAP improvement.

  * •

Enhanced bounding box coordinate prediction is achieved by predicting location
coordinates in relation to grid cell locations, leading to a 5% increase in
mAP with more uniform bounding box aspect ratios and sizes.

  * •

YOLOv2 employs convolutional layers for feature extraction and predicts
bounding boxes using anchor boxes, contributing to a 7% improvement in recall.

  * •

The adoption of a clustering algorithm based on K-means eliminates the need
for manual selection of anchor boxes, thereby enhancing accuracy.

  * •

To tackle the challenge of smaller object detection, skip connections inspired
by ResNet are incorporated, resulting in a 1% increase in mAP. For instance, a
26 x 26 x 512 feature map transforms into a 13 x 13 x 2048 feature map,
concatenating with the model’s output, enabling more robust object recognition
across various dimensions.

###  4.3 YOLOv3

The introduction of YOLOv3 [39] in 2018 by Joesph Redmon et al. represented a
significant evolution, featuring an expanded architecture outlined in Table 2.
This iteration embraced contemporary technological advancements while
maintaining real-time processing capabilities. Similar to YOLOv2, YOLOv3
predicts four coordinates for each bounding box but introduces an objectness
score for each box, determined through logistic regression. This score assumes
values of 1 or 0, indicating whether the anchor box has the highest overlap
with the ground truth (1) or other anchor boxes (0). Unlike Faster R-CNN [40],
YOLOv3 associates a single anchor box with each ground truth object, incurring
only the classification loss in cases where no anchor box is associated,
excluding localization and confidence losses.

Figure 4: Multi-scale Detection Architecture Table 2: YOLOv3 Architecture Layer | Filters | Size | Repeat | Output Size
---|---|---|---|---
Image | — | — | — | 416 $\times$ 416
Conv | 32 | 3 $\times$ 3 / 1 | 1 | 416 $\times$ 416
Conv | 64 | 3 $\times$ 3 / 2 | 1 | 208 $\times$ 208
Conv | 32 | 1 $\times$ 1 / 1 | Conv $\times$ 1 | 208 $\times$ 208
Conv | 64 | 3 $\times$ 3 / 1 | Conv $\times$ 1 | 208 $\times$ 208
Residual | — | — | Residual $\times$ 1 | 208 $\times$ 208
Conv | 128 | 3 $\times$ 3 / 2 | 1 | 104 $\times$ 104
Conv | 64 | 1 $\times$ 1 / 1 | Conv $\times$ 2 | 104 $\times$ 104
Conv | 128 | 3 $\times$ 3 / 1 | Conv $\times$ 2 | 104 $\times$ 104
Residual | — | — | Residual $\times$ 2 | 104 $\times$ 104
Conv | 256 | 3 $\times$ 3 / 2 | 1 | 52 $\times$ 52
Conv | 128 | 1 $\times$ 1 / 1 | Conv $\times$ 8 | 52 $\times$ 52
Conv | 256 | 3 $\times$ 3 / 1 | Conv $\times$ 8 | 52 $\times$ 52
Residual | — | — | Residual $\times$ 8 | 52 $\times$ 52
Conv | 512 | 3 $\times$ 3 / 2 | 1 | 26 $\times$ 26
Conv | 256 | 1 $\times$ 1 / 1 | Conv $\times$ 8 | 26 $\times$ 26
Conv | 512 | 3 $\times$ 3 / 1 | Conv $\times$ 8 | 26 $\times$ 26
Residual | — | — | Residual $\times$ 8 | 26 $\times$ 26
Conv | 1024 | 3 $\times$ 3 / 2 | 1 | 13 $\times$ 13
Conv | 512 | 1 $\times$ 1 / 1 | Conv $\times$ 4 | 13 $\times$ 13
Conv | 1024 | 3 $\times$ 3 / 1 | Conv $\times$ 4 | 13 $\times$ 13
Residual | — | — | Residual $\times$ 4 | 13 $\times$ 13

In contrast to utilizing SoftMax for classification, YOLOv3 employs binary
cross-entropy, enabling the assignment of multiple labels to a single box. The
architecture integrates an extensive feature extractor with 53 convolutional
layers and incorporates residual connections.

Significant improvements involve a modified spatial pyramid pooling (SPP)
block within the backbone to accommodate a broader receptive field. YOLOv3
organizes feature maps into three scales: (416$\times$416), (13$\times$13),
(26$\times$26), and (52$\times$52) for input, each featuring three prior boxes
for every position (as depicted in Figure 4). Collectively, these enhancements
resulted in a 2.7% improvement in the AP50 metric.

The determination of eight prior boxes, distributed across the three-scale
feature maps, employs the K-means algorithm. Larger-scale feature maps
incorporate smaller precedent boxes. The foundational architecture of YOLOv3
referred to as Darknet-53, replaces all max-pooling layers with stride
convolutions and integrates residual connections. Comprising 53 convolutional
layers (as detailed in Table 2), this backbone architecture emerged as the
primary benchmark for object detection shifted from PASCAL VOC [41] to
Microsoft COCO [42]. Consequently, all subsequent YOLO models were evaluated
using the MS COCO dataset. YOLOv3 achieved notable results: an average
precision (AP) of 36.2% and an AP-50 of 60.6% at a processing speed of 20
frames per second (FPS), surpassing the pace of previous state-of-the-art
models.

###  4.4 YOLOv4

In April 2020, a team led by Alexey Bochkovskiy introduced YOLOv4 [43],
representing a profound departure from its predecessors with revolutionary
architectural changes aimed at improving performance while maintaining real-
time capabilities. The key advancements in YOLOv4 include the integration of
CSP Darknet53, SPP structure [44], PANet architecture [45] (depicted in Figure
5), CBN integration [46], and SAM incorporation [47], resulting in an
efficient and robust object detection model. Designed to simplify the training
of object detectors, YOLOv4 aims to be accessible to individuals with varying
technical expertise. The study also validated the effectiveness of state-of-
the-art methodologies, such as bag-of-freebies and bag-of-specials, to enhance
the efficiency of the training pipeline.

Unlike YOLOv3, where a single anchor point detected a ground truth, YOLOv4
uses multiple anchor points for a single ground truth detection. This approach
improves the selection ratio of positive samples, reduces the imbalance
between positive and negative samples, and enhances boundary detection
accuracy.

Figure 5: Path Aggregation YOLOv4 (a) Addition (b) Concatenation

YOLOv4 employs the Complete Intersection over Union (CIoU) loss, depicted in
Equation (8), to refine localization accuracy by incorporating factors like
IoU, maximum IoU, and regularization. The utilization of this loss function
enhances YOLOv4’s capability to accurately locate and outline objects in
images, thereby improving overall object detection performance.

$\displaystyle
L_{CIoU}=1-\text{IoU}(b,\hat{b})+\frac{(\rho^{2}-\text{IoU}(b,\hat{b})^{2})}{\rho^{2}}$
(8) $\displaystyle\quad+\alpha\cdot\frac{v}{(1-\text{IoU}(b,\hat{b})+v)}$

Here, $L_{CIoU}$ denotes the CIoU loss, $b$ stands for the predicted bounding
box, $\hat{b}$ represents the ground truth bounding box,
$\text{IoU}(b,\hat{b})$ computes the Intersection over Union (IoU) between the
predicted and ground truth boxes, $\rho^{2}$ serves as a parameter for the
maximum possible IoU, $\alpha$ acts as a balancing factor, and $v$ is employed
to address small bounding boxes.

###  4.5 YOLOv5

In 2020, Glenn Jocher introduced YOLOv5, following the release of YOLOv4 [46].
YOLOv5, managed by Ultralytics, takes a different path from YOLOv4 in several
key aspects. Notably, YOLOv5 opts for PyTorch instead of Darknet for
development, expanding its user base due to PyTorch’s user-friendly
characteristics. YOLOv5 incorporates various enhancements to improve its
performance in object detection. At its core, YOLOv5 features a CSP (Cross
Stage Partial) Net, derived from the ResNet architecture, which includes a
cross-stage partial connection for enhanced network efficiency. The CSPNet is
complemented by multiple SPP (Spatial Pyramid Pooling) blocks for feature
extraction at different scales.

The architecture’s neck includes a PAN (Path Aggregation Network) module and
subsequent upsampling layers to improve feature map resolution [48]. The head
of YOLOv5 utilizes convolutional layers to predict bounding boxes and class
labels. YOLOv5 employs anchor-based predictions, associating each bounding box
with predetermined anchor boxes of specific shapes and sizes. The loss
function in YOLOv5 combines Binary Cross-Entropy and Complete Intersection
over Union (CIoU) for class, objectness, and localization losses, expressed as
(9):

$\text{loss}=\lambda_{1}\cdot L_{\text{cls}}+\lambda_{2}\cdot
L_{\text{obj}}+\lambda_{3}\cdot L_{\text{loc}}$ (9)

Where $L_{\text{cls}}$, $L_{\text{obj}}$, and $L_{\text{loc}}$ denote the
Binary Cross-Entropy loss for class predictions, Binary Cross-Entropy loss for
objectness predictions, and CIoU loss for localization, respectively. The
$\lambda$ values serve as weighting factors for each loss component.

The primary objective of YOLOv5 is to enhance efficiency and accuracy,
surpassing its predecessors. It brings advancements in feature extraction,
feature aggregation, and anchor-based predictions. Furthermore, it ensures a
seamless transition from PyTorch to ONNX and CoreML frameworks, enhancing
compatibility with iOS devices. In evaluations on the MS COCO dataset’s test-
dev 2017 split, YOLOv5x achieved an average precision (AP) score of 50.7% with
a 640-pixel image size, processing at a high speed of 200 frames per second
(FPS) on an NVIDIA V100. With a larger input size of 1536 pixels, YOLOv5
achieved an even higher AP score of 55.8%, as evident from Table 3.

Table 3: Variant Comparison of YOLOv5 Model |  Average Precision (@50) |  Parameters |  FLOPs
---|---|---|---
YOLO-v5s |  55.8% |  7.5M |  13.2B
YOLO-v5m |  62.4% |  21.8M |  39.4B
YOLO-v5l |  65.4% |  47.8M |  88.1B
YOLO-v5x |  66.9% |  86.7M |  205.7B

###  4.6 YOLOv6

Released in September 2022 by the Meituan Vision AI Department, YOLOv6 is a
single-stage object detection framework designed specifically for industrial
applications. This version brings significant improvements and architectural
refinements, notably introducing CSPDarknet as the new backbone architecture,
surpassing its predecessors’ efficiency and speed benchmarks, YOLO-v4 and
YOLO-v5. One key enhancement in YOLO-v6 is the integration of a feature
pyramid network (FPN), expanding the range of feature scales and resulting in
a noticeable improvement in detection accuracy. This underscores the
commitment to enhancing overall performance [49].

YOLO-v6 is meticulously crafted for optimal real-time object detection
performance, demonstrating impressive frame rates on both central processing
units (CPUs) and graphics processing units (GPUs).A pivotal evolution in the
YOLOv6 architecture involves the separation of the classification and box
regression heads, as illustrated in Figure 6. This strategic architectural
revision introduces additional layers within the network, effectively
segregating these crucial functions from the final head [50]. Empirical
evidence supports the impact of this refinement in elevating the overall
model’s performance, reinforcing its capabilities [51].

Figure 6: PANet Configuration [2]

In aggregate, YOLOv6 marks a substantial advancement in the progression of
YOLO architectures, incorporating a wide range of enhancements that span
speed, accuracy, and operational efficiency. A thorough assessment of the MS
COCO dataset’s test-dev 2017 subset highlighted the capabilities of the
YOLOv6L model, delivering an average precision (AP) of 52.5% and an AP50 of
70%. Notably, this commendable performance was attained while sustaining a
processing speed of around 50 frames per second (FPS) on an NVIDIA Tesla T4
GPU. YOLOv6 is presented in three distinct variants, as outlined in Table 4.
Notably, YOLOv6nano stands out as the smallest and fastest alternative,
characterized by a minimal parameter count. This feature makes it particularly
well-suited for real-time object detection tasks on devices with limited
computational capabilities. Advancing to YOLO-v6tiny, this variant offers a
more expansive architecture compared to YOLOv6nano, resulting in increased
accuracy, as evidenced in Table 4. YOLOv6tiny proves valuable when precision
is crucial, especially in scenarios involving the detection of smaller
objects.

Table 4: YOLOv6 Variant Comparison v6 Variant |  Size |  mAP |  Parameters |  FLOPs
---|---|---|---|---
Nano |  416-640 |  30.8-35.0% |  4.3M |  4.7-11.1G
Tiny |  640 |  41.3% |  15M |  36.7G
Small |  640 |  43.1% |  17.2M |  44.2G

Conversely, YOLOv6small leads in architectural complexity, delivering a higher
level of accuracy. This configuration is particularly suitable for scenarios
where detecting smaller objects within the visual field is paramount. The
selection among these variants depends on the specific use case and available
computational resources. YOLOv6nano is an optimal choice for scenarios
requiring real-time detection on low-powered devices, while preferences for
YOLOv6tiny or YOLOv6small may arise in instances where greater accuracy and
the identification of smaller objects are essential. The decision should be
tailored to the available resources and the desired accuracy threshold.

###  4.7 YOLOv7

Introduced in July 2022, YOLOv7 [52] marked a significant leap forward from
its predecessors, showcasing improved accuracy and speed enhancements ranging
from 5 FPS to 160 FPS. These advancements primarily centred on boosting
efficiency and scalability by integrating the Extended Efficient Layer
Aggregation Network (E-ELAN) [53] and implementing a scalable concatenation-
based architecture. E-ELAN plays a pivotal role in managing the gradient path,
thereby augmenting model learning and convergence. This technique is versatile
and applicable to models with stacked computational blocks, adeptly shuffling
and merging features from different groups while preserving the integrity of
the gradient path. Model scaling is another critical aspect of YOLOv7,
enabling the creation of models of varying sizes. The devised scaling strategy
adjusts the depth and width of the blocks uniformly, maintaining the optimal
model structure while mitigating hardware resource consumption. The
amalgamation of various techniques, collectively termed "bag-of-freebies,"
further amplifies YOLOv7’s performance. One such technique mirrors the re-
parameterized convolution concept employed in YOLOv6. However, the RepConvN
approach was introduced in YOLOv7 due to identified issues with the identity
connection in RepConv [54] and concatenation in DenseNet [55].

Additionally, YOLOv7 utilizes coarse label assignment for the auxiliary head,
reserving fine label assignment for the lead head. While the auxiliary head
contributes to the training process, the lead head produces the final output,
as shown in Figure 6. Moreover, batch normalization is employed, amalgamating
the mean and variance of batch normalization into the convolutional layer’s
bias and weight during inference, ultimately enhancing model performance [56].
Under rigorous evaluation on the MS COCO dataset’s test-dev 2017, YOLOv7E6
demonstrated outstanding performance, achieving an average precision (AP) of
55.9% and an AP for the IoU threshold of 0.5 (AP50) of 73.5%, as showcased in
Table 5.

Table 5: YOLOv7 Variant Comparison v7 Variant |  Size |  mAP |  Parameters |  FLOPs
---|---|---|---|---
YOLO-v7 tiny |  640 |  52.8% |  6.2M |  5.8G
YOLO-v7 |  640 |  69.7% |  36.9M |  104.7G
YOLO-v7X |  640 |  71.1% |  71.3M |  189.9G
YOLO-v7E6 |  1280 |  73.5% |  97.2M |  515.2G
YOLO-v7D6 |  1280 |  73.8% |  154.7M |  806.8G

###  4.8 YOLOv8

In January 2023, Ultralytics introduced YOLOv8, making a significant entrance
into the field of computer vision [57]. The model’s precision was extensively
assessed through evaluations on both COCO and Roboflow 100 datasets [57].
YOLOv8 distinguishes itself with user-oriented features, including a user-
friendly command-line interface and a well-organized Python package. The
supportive YOLO community further enhances the model’s accessibility for
users. The innovation in YOLOv8, as detailed in its methodology [58], diverges
from traditional anchor-based methods. Instead of relying on predetermined
anchor boxes, YOLOv8 adopts an anchor-free approach by predicting the object’s
centre. This adjustment addresses challenges associated with anchor boxes that
may not accurately represent custom dataset distributions. This approach’s
advantages include reducing the number of box predictions and expedited post-
processing steps involving Non-Maximum Suppression. Notably, YOLO-v8’s
training routine, which incorporates techniques like online image
augmentation, including mosaic augmentation, enhances the model’s ability to
detect objects across diverse conditions and novel spatial arrangements.

In its architectural evolution from its predecessor, YOLOv5 (also authored by
the same individuals), YOLOv8 introduces changes across its components. For
instance, in the neck segment, YOLOv8 directly concatenates features without
enforcing uniform channel dimensions. This strategy contributes to a reduction
in parameter count and overall tensor size. When assessed on the MS COCO
dataset’s test-dev 2017 subset, YOLOv8x demonstrated an average precision (AP)
of 53.9% at an image size of 640 pixels, surpassing YOLOv5’s AP of 50.7% with
the same input size. Furthermore, YOLOv8x exhibited remarkable processing
speed, achieving 280 frames per second (FPS) using an NVIDIA A100 with
TensorRT. Notably, YOLOv8 is available in five distinct variants, each
tailored to specific accuracy and computational requirements, as detailed in
Table 6.

Table 6: YOLO-v8 Variant Comparison Model |  Size |  mAP |  Parameters |  FLOPs
---|---|---|---|---
YOLO-v8n |  640 |  37.3% |  3.2M |  8.7G
YOLO-v8s |  640 |  44.9% |  11.2M |  28.6G
YOLO-v8m |  640 |  50.2% |  25.9M |  78.9G
YOLO-v8l |  640 |  52.9% |  43.7M |  165.2G
YOLO-v8x |  640 |  53.9% |  68.2M |  257.8G

###  4.9 YOLOv9

YOLOv9, released in February 2024 [59] represents the latest addition to the
mainstream YOLO variants. YOLOv9 boasts two key innovations: the Programmable
Gradient Information (PGI) framework and the Generalized Efficient Layer
Aggregation Network (GELAN). The PGI framework aims to address the issue of
information bottlenecks, inherent in deep neural networks in addition to
enabling deep supervision mechanisms to be compatible with lightweight
architectures. By implementing PGI, both lightweight and deep architectures
can leverage substantial improvements in accuracy, as PGI mandates reliable
gradient information during training, thus enhancing the architecture’s
capacity to learn and make accurate predictions.

The GELAN architecture was purposefully designed to boost performance in
object detection tasks via high efficiency and lightweight footprint. GELAN
manifests high performance across varying computational blocks and depth
configurations, making it suitable for deployment on different inference
devices, including resource-constrained edge devices. By combining the above
two frameworks (PGI and GELAN), YOLOv9 presents a significant advancement in
lightweight object detection. Although in its early days, YOLOv9 has achieved
remarkable competitiveness in object detection tasks, outperforming YOLOv8
concerning parameter reduction and computational efficiency while improving
Average Precision (AP) by 0.6% on the MS COCO dataset.

###  4.10 YOLOv10

Building upon the momentum of innovation in the YOLO series, another
groundbreaking development emerged in the same year with the release of
YOLOv10, also in 2024. This version pushes the boundaries further by
addressing the challenges associated with real-time object detection, which is
critical for applications requiring rapid and accurate responses, such as
agricultural monitoring and autonomous vehicle navigation.

YOLOv10 distinguishes itself by completely eliminating the reliance on non-
maximum suppression (NMS) during post-processing, which is a significant step
forward in enhancing inference speed. This model adopts a novel NMS-free
training approach using dual label assignments, allowing for a harmonious
integration of accuracy and speed by ensuring that the model remains
computationally efficient while still capturing essential detection features.
Moreover, the architectural enhancements in YOLOv10 include the implementation
of lightweight classification heads, spatial-channel decoupled downsampling,
and rank-guided block design, each contributing to substantial reductions in
computational demands and parameter count. These innovations not only improve
the model’s efficiency but also its scalability across various devices, from
high-power servers to resource-limited edge devices.

Extensive testing demonstrates that YOLOv10 sets a new benchmark for the
performance-efficiency trade-off. It achieves remarkable improvements in
latency and model size reduction compared to YOLOv9, while still delivering
competitive or superior detection accuracy. This is particularly evident in
its application to the COCO dataset, where YOLOv10 shows notable advancements
in detection metrics, solidifying its position as a leader in the field of
real-time object detection technologies [60].

Table 7 provides a comparative overview of the major YOLO variants up to the
current date. The table illustrates the iterative evolution of the YOLO series
of object detectors, with each iteration advancing the state-of-the-art in
computer vision.

Table 7: YOLO Variant Comparison Version | Date |  Contributions | Framework
---|---|---|---
v1 | 2015 |  One-shot object detector | Darknet
v2 | 2016 |  Multi-scale training, dimensional clustering | Darknet
v3 | 2018 |  SPP block, Darknet-53 | Darknet
v4 | 2020 |  Mish-based activation, CSPDarknet-53 backbone | Darknet
v5 | 2020 |  Anchor-free detection, SWISH-based activation, PANet | PyTorch
v6 | 2022 |  Self-attention, anchor-free object detection | PyTorch
v7 | 2022 |  Transformers, E-ELAN reparameterization | PyTorch
v8 | 2023 |  GANs, anchor-free detections | PyTorch
v9 | 2024 |  Programmable Gradient Information (PGI), Generalized Efficient Layer Aggregation Network (GELAN) | PyTorch
v10 | 2024 |  NMS-free training approach, dual label assignments, holistic model design for enhanced accuracy and efficiency | PyTorch

##  5 Agricultural Applications of YOLO

In this section, we provide a comprehensive review of the current body of
literature regarding the utilization of different YOLO variants for various
agricultural applications. The discussion is tailored to include applications
such as weed detection, crop classification, disease detection, animal
tracking, and precision farming.

###  5.1 Weed Detection Using YOLO

Weed detection is a crucial aspect of modern agriculture as it directly
impacts crop yield and resource optimization. Traditional methods of weed
management can be labour-intensive and time-consuming. The integration of YOLO
variants in weed detection brings forth a promising solution by offering real-
time and efficient identification of weeds in agricultural landscapes.

This subsection explores the application of YOLO in weed detection, focusing
on its potential to revolutionize weed management practices. We delve into the
challenges associated with weed detection in traditional agriculture and
examine how YOLO variants address these challenges. Additionally, we explore
real-world scenarios where YOLO has demonstrated efficacy in accurately
identifying and localizing weeds, contributing to advancements in precision
farming.

[61] developed and implemented a real-time weed detection system targeting
green onion crops. Leveraging the YOLOv3 deep learning algorithm, the system,
coined YOLO-WEED, demonstrated notable efficiency and precision in identifying
weeds within video frames captured by Unmanned Aerial Vehicles (UAVs) [61].
This positions YOLO-WEED as a valuable asset for precise agricultural
activities such as targeted spraying and weed management. The system’s
performance evaluation, based on mean average precision and F1 score metrics,
yielded impressive results with a mean average precision of 93.81% and an F1
score of 0.94. The notable swiftness and accuracy of the YOLO-WEED system make
it a promising candidate for seamless integration into real-time automated
aerial spray systems specifically designed for green onion fields [61].
However, it is important to acknowledge certain limitations. The system’s
effectiveness is contingent upon the resolution of UAV video frames. Notably,
the YOLOv3 algorithm employed in the system encounters challenges in detecting
smaller objects, presenting difficulties in identifying diminutive weeds
within the green onion fields [61]. Moreover, the YOLO-WEED system
necessitates the presence of an onboard computer, introducing an additional
weight factor to the UAV sprayer system. Despite these considerations, the
system’s overall performance underscores its potential as an impactful
solution for advancing precision farming practices, particularly in the
context of weed detection and control in green onion cultivation.

Boyu Ying et al. conducted a meticulous study on detecting weeds in images of
carrot fields using an improved YOLOv4 model [62]. The researchers collected
test images from the carrot fields in Central China’s Henan Province and aimed
to detect four common field weeds: crabgrass, plantain, pale persicaria, and
cephalanoplos. They developed a lightweight weed detection model called
YOLOv4-weeds, which replaced the backbone network of YOLOv4 with
MobileNetV3-Small. This modification reduced the memory requirement of image
processing and improved the efficiency and accuracy of small weed detection in
complex environments. The authors conducted comparative experiments with other
detection models and demonstrated that the YOLOv4-weeds model outperformed
these models, especially in detecting diverse weeds in complex field scenes.
The research outcomes provide valuable insights and a reference for weed
detection, robot weeding, and selective spraying in agricultural settings.
However, the study has potential limitations, such as the diversity of weed
species, generalization to other crops, robustness to environmental
variability, real-world deployment and validation, and consideration of
computational resources. These findings significantly affect the agricultural
sector, as weed management is critical to crop yield and quality. The study
provides a framework for improving weed detection in complex agricultural
environments, which can lead to efficient and cost-effective weed management
practices. However, further research is required to address the study’s
limitations and enhance the generalizability of the findings to other crops
and environmental settings.

Dyrmann et al. conducted a study focusing on the identification and tracking
of invasive alien plant species (IAPS) along state roads, utilizing a camera-
based monitoring system [63], as shown in Figure 7. The employed deep learning
algorithms successfully detected and classified IAPS in the collected images,
demonstrating promising results for real-time mapping of invasive alien plant
species at driving speeds of 110 km/h. Despite notable achievements, the study
acknowledges certain limitations, such as challenges in detecting specific
plant species like Lupinus polyphyllus and Pastinaca sativa, and emphasizes
the need for addressing these issues in future research. The study provides
valuable insights for cost-effective and efficient environmental conservation
along roadsides.

Figure 7: Plant Species Detection On Device Deployment

Chen et al. concentrated on developing a YOLO-sesame model for weed detection
in sesame fields [64]. The model, incorporating an improved attention
mechanism and feature fusion, exhibited high performance in terms of Frames
Per Second (FPS) and mean Average Precision (mAP). While showcasing promising
results, the study highlights regional specificity in the dataset and
emphasizes the necessity for further work to enhance the model’s applicability
to embedded devices. This work contributes to the advancement of weed
detection methodologies in sesame cultivation.

Wang et al. conducted field trials in Zhangjiakou City, Hebei Province, to
test their method for real-world application [65]. The study’s technique for
image preprocessing and the deployment of an improved YOLOv5 CNN showed
satisfactory performance in practical scenarios. Despite notable achievements,
the study recognizes limitations related to environmental conditions and
underscores the importance of addressing these challenges for broader
applicability. The study contributes to the progress of weed detection
methodologies with potential implications for the early-stage identification
of invasive weeds.

Costello et al. investigated the use of RGB and HSI data for the field mapping
of Parthenium weed in a controlled environment [66]. Employing deep-learning
algorithms, including decision-tree-based protocols, the study achieved high
accuracy in detecting and categorizing Parthenium weed growth stages. However,
the study acknowledges limitations such as the controlled environment and
emphasizes the need for further exploration of AI algorithms and technology
improvements for enhanced detection success. This research offers valuable
insights into the application of AI and imaging techniques for Parthenium weed
detection, with potential implications for weed management and agriculture.

Table 8: Studies on Weed Detection Using YOLO

Authors |  Model |  Details
---|---|---
[61] |  YOLOv3 |  Real-time weed detection with UAVs in green onion fields. Mean average precision of 93.81%, F1 score of 0.94.
[62] |  YOLOv4 |  Detection of weeds in carrot fields. Efficient detection of diverse weeds, outperforming other models in complex field scenes.
[63] |  YOLO |  Identification and tracking of invasive alien plant species (IAPS) along Danish state roads. Successful results at driving speeds of 110 km/h.
[64] |  YOLO-custom |  Weed detection in sesame fields with improved attention mechanism and feature fusion. High FPS and mAP.
[65] |  YOLOv5 |  Field trials in Zhangjiakou City, Hebei Province, for weed detection. Satisfactory performance in practical scenarios.
[66] |  YOLOv4 |  Field mapping of Parthenium weed in a controlled environment. High accuracy in detecting and categorizing Parthenium weed growth stages.
[67] |  YOLOv4 & YOLOv5 |  Introduction of YOLOWeeds benchmark dataset for detecting various weed types in cotton production systems. Evaluation of six different YOLO object detection models.
[68] |  YOLO |  Detection of poppy (Papaver rhoeas) in wheat fields. Approximate 75% accuracy.
[69] |  YOLO |  Evaluation for weed detection in various turfgrass scenarios. Emphasis on challenges associated with accurate weed detection in turfgrass.
[70] |  YOLO-v3 |  Novel method for weed detection in vegetable fields using a two-stage approach based on Convolutional Neural Networks (CNN).

Dang et al. introduced the YOLOWeeds benchmark dataset for detecting various
weed types in cotton production systems [67]. Evaluating six different YOLO
object detection models, the study provides a detailed experimental setup,
emphasizing challenges in weed management within cotton production. The
results showcase the potential of YOLOv4 and YOLOv5 for real-time weed
detection, prompting further exploration in machine vision-based weeding
systems. This work contributes to the advancement of automated weed
identification with potential applications in sustainable weed management in
agriculture.

Pérez-Porras et al. conducted a study on detecting poppy (Papaver rhoeas) in
wheat fields using YOLO architectures [68]. Evaluating different YOLO models,
the study optimized hyperparameters and assessed computational efficiency.
Despite achieving an accuracy of approximately 75%, the study acknowledges the
need for field validation and integration with agricultural practices. This
research provides valuable insights into early weed detection in agricultural
fields, specifically in the case of poppy detection in wheat fields.

Sportelli et al. evaluated the performance of YOLO object detectors for weed
detection in various turfgrass scenarios [69]. Utilizing three datasets with
specific characteristics, the study underscores the challenges associated with
accurate weed detection in turfgrass. While achieving high performance, the
study acknowledges limitations in model performance and emphasizes the need
for further research to address these challenges. This work contributes to a
comprehensive understanding of the trade-offs between model performance and
computational efficiency in weed detection.

Jin et al. proposed a novel method using YOLO-v3 to identify weeds in
vegetable fields [70]. Adopting a two-stage approach based on Convolutional
Neural Networks (CNN), the method accurately detects weeds by focusing on the
detection of vegetable crops. Despite promising results, the study highlights
the primary limitation in the lack of robust sensing technology for the
commercial development of intelligent robotic weeders. This research provides
a feasible approach for weed detection in various crops and emphasizes the
need for advancements in sensing technology for broader applications. Table 8
presents a compilation of studies employing diverse YOLO architectures for the
detection of weeds in the agricultural sector.

###  5.2 Crop Detection via YOLO

In the domain of precision agriculture, crop detection plays a pivotal role in
optimizing farming practices and resource utilization. Accurate identification
and delineation of crops in large-scale fields contribute to improved
monitoring, yield estimation, and resource management. Various studies have
explored the application of YOLO variants to address the challenges associated
with crop detection, showcasing the potential for real-time, efficient, and
precise identification of different crop types.

This subsection provides an overview of notable research endeavours that
employ YOLO models for crop detection. The studies discussed herein present
advancements, methodologies, and outcomes related to the utilization of YOLO-
based approaches in identifying and delineating crops in diverse agricultural
settings. The significance of robust crop detection methodologies in the
context of precision farming is emphasized, highlighting how YOLO variants
contribute to addressing the evolving needs of modern agriculture.

Tian et al. introduced an enhanced YOLO-V3 model designed for real-time apple
detection in orchards [71]. Employing a camera with a 3000 x 4000-pixel
resolution under varying weather and illumination conditions, the researchers
gathered image data. Data augmentation techniques were applied to augment the
dataset, enhancing its diversity. To optimize feature layers with low
resolution in the YOLO-V3 network, the authors incorporated the DenseNet
method, aiming to boost feature propagation, encourage reuse, and enhance
overall network performance. The proposed YOLOV3-dense model exhibited
superior performance compared to the original YOLO-V3 and the Faster R-CNN
with VGG16 net model, particularly in terms of detection accuracy and real-
time capabilities. Noteworthy, the study concentrated exclusively on apple
detection, neglecting exploration into other fruits or crops. Future
investigations could explore the adaptability of the proposed model to diverse
agricultural contexts. Furthermore, the study overlooked the potential impact
of adverse weather conditions, such as rain or fog, on the model’s
performance. Subsequent research endeavours could delve into assessing the
robustness of the model under various weather scenarios. Lastly, the study’s
evaluation on a large-scale dataset was lacking. Consequently, forthcoming
studies may consider validating the model on a more extensive dataset to
further establish and validate its performance.

Sharpe and the research team developed a precision applicator for effective
goosegrass control in Florida’s vegetable plasticulture production [72]. The
study assessed the utilization of the YOLOv3-tiny detector for on-site
goosegrass detection and spraying. Image processing involved various plants,
including strawberry and tomato plants, as well as other weed species, to
train and test the neural network. While showcasing the potential of
convolutional neural networks in horticultural crop weed detection and
management, the study identifies specific limitations and areas for
improvement. Primarily, the focus on goosegrass detection in strawberry and
tomato production warrants further research to extend the applicability of the
network to diverse crops and weed species. The study highlights the
superiority of the LB annotation method for production and precision spraying
but suggests that additional classes or grouping may enhance overall network
accuracy. Lastly, for tomatoes, the study acknowledges limitations in the
piecewise image methodology, urging further research to enhance detection
accuracy for this particular crop.

Junos et al. introduced YOLO-P, an object detection model capable of
identifying and locating objects (FFB, grabber, and palm tree) in oil palm
plantations [73]. Through multiple experiments, the proposed model
demonstrated outstanding mean average precision and F1 scores of 98.68% and
0.97, respectively. Characterized by a faster training process and lightweight
design (76 MB), the model exhibited accuracy in identifying fresh fruit
bunches at various maturities, offering potential applications in automated
crop harvesting systems. The comprehensive experimental results suggest that
YOLO-P can accurately and robustly detect objects in palm oil plantations,
thereby contributing to increased productivity and optimized operational costs
in the agricultural industry.

Chen et al. made significant strides in citrus fruit detection with the
development of the CitrusYOLO algorithm [74]. Enhancements to the YOLOv4 model
included the addition of a 152*152 feature detection layer, dense connections
for multiscale fusion, and integration of depthwise separable convolution and
attention mechanism modules. These improvements resulted in increased
detection accuracy and real-time performance. CitrusYOLO exhibited superior
performance, outperforming standard deep learning algorithms in terms of
accuracy and time efficiency. Despite these advancements, the study recognizes
certain limitations and areas for improvement. The dataset’s focus on two
varieties of Kumquats and Nanfeng tangerines and four varieties of Fertile
orange, Tangerine, Mashui orange, and Gonggan suggests the potential for
improved performance with expanded citrus varieties and growth stages. The
algorithm’s performance under varied lighting conditions and its applicability
to other fruits or objects in different environments remain unknown. While
experiments demonstrated effectiveness, real-world applications such as
orchard yield estimation and fruit harvesting robots require further
validation.

Hong et al. developed a lightweight model for detecting wheat ear Fusarium
head blight (FHB) using RGB images [75]. Leveraging YOLOv4 and MobileNet
architectures, the proposed model struck a balance between accuracy and real-
time FHB detection. With an accuracy of 93.69% in detecting wheat ear FHB,
outperforming the MobileNetv2-YOLOv4 model, the suggested model’s reduced size
facilitates deployment on uncrewed aerial vehicles (UAVs). While demonstrating
great potential for real-time FHB detection, the study acknowledges certain
limitations and areas for improvement. The study’s exclusive focus on wheat
ear Fusarium head blight detection, overlooking other diseases affecting wheat
crops, suggests future research considerations for expanding the model’s
disease detection capabilities. Challenges related to incorrect detections of
small objects and detection performance in complex backgrounds highlight the
need for refining the model’s performance. Additionally, the study’s emphasis
on reducing parameters necessitates exploration of the model’s generalization
across different edge platforms.

Wang et al. proposed a real-time method for vehicle identification and
tracking using Improved YOLOv4 and binocular positioning (BPO) [76].
Addressing the tracking problem in agricultural master-slave follow-up
operations, the study’s experiments showcased the method’s accuracy in
identifying and tracking the master vehicle in real time. Low RMS errors in
terms of longitudinal, lateral, and heading angle deviation signify the
method’s effectiveness in meeting the positioning requirements of the master
vehicle. The study also created a dataset for training and testing the
identification model, hinting at future work on constructing and testing
enslaved person automatic follow-up systems. While the study does not
explicitly mention limitations, potential areas for further exploration
include the validation of the method in diverse agricultural environments and
consideration of its performance under varying weather and lighting
conditions. Table 9 compiles a summary of research endeavours utilizing
diverse YOLO architectures for crop detection within the agricultural domain.

Table 9: Studies on Crop Detection Using YOLO

Author |  Model |  Details
---|---|---
[71] |  YOLOv3-dense |  Enhanced YOLO-V3 model for real-time apple detection in orchards. Superior performance in detection accuracy and real-time capabilities.
[72] |  YOLOv3-tiny |  Utilization of YOLOv3-tiny detector for on-site goosegrass detection and spraying in vegetable plasticulture production. Potential for convolutional neural networks in horticultural crop weed detection and management.
[73] |  YOLO-P |  Introduction of YOLO-P for identifying and locating objects (FFB, grabber, and palm tree) in oil palm plantations. Outstanding mean average precision (98.68%) and F1 score (0.97).
[74] |  Citrus-YOLO |  Development of CitrusYOLO algorithm for citrus fruit detection. Improved YOLOv4 model with increased detection accuracy and real-time performance.
[75] |  YOLOv4 |  Lightweight model for detecting wheat ear Fusarium head blight (FHB) using RGB images. YOLOv4 and MobileNet architectures for a balance between accuracy and real-time FHB detection.
[76] |  YOLOv |  Real-time method for vehicle identification and tracking using Improved YOLO (IYO) v4 and binocular positioning (BPO). Accuracy in identifying and tracking master vehicles in agricultural master-slave follow-up operations.

###  5.3 Animal Tracking with YOLO

In recent years, the application of You Only Look Once (YOLO) object detection
models has revolutionized animal tracking in ecological research. YOLO’s real-
time and high-precision capabilities make it a compelling tool for monitoring
wildlife, enabling the automatic identification and tracking of animals in
diverse environments. This subsection delves into the innovative use of YOLO-
based systems for animal tracking, exploring their contributions to
understanding animal behaviour, migration patterns, and ecological dynamics.

Wang et al. conducted a study to monitor and analyze the behaviours of egg
breeders in self-breeding cages using visual image processing and the YOLO v3
deep learning algorithm [77]. Their approach identified six behaviours,
achieving high precision rates and offering insights into the welfare state of
egg breeders. Despite the success, the study’s limitation involves the
analysis of a limited sample size of Hy-Line Gray egg breeders in a single
cage, warranting further investigation across diverse breeds, larger
populations, and comparisons with existing methods for behaviour recognition
[77].

Schütz et al. applied YOLOv4 for red fox detection and motion monitoring,
showcasing the potential of computer vision systems in studying animal
behaviour [78]. While the study emphasized the efficiency and accuracy of
computer evaluation, limitations included occasional camera manipulation by
foxes and challenges in detecting rarely occurring fox positions not present
in the training set. Proposed solutions involve securing the camera
appropriately and expanding the training set to address bias and improve
accuracy [78].

Yu et al. utilized the YOLO-improved model and edge computing to detect dairy
cow feeding behaviour automatically [79]. The proposed DRN-YOLO algorithm
exhibited improved precision, recall, mean Average Precision (mAP), and
F1-score, with potential areas for improvement outlined, such as further
subdividing cow foraging behaviour and testing generalizability across diverse
farm environments and cow populations [79].

Elmessery et al. conducted a comprehensive study to develop and validate a
YOLOv8-based model for automatically detecting broiler pathological phenomena
in intensive poultry houses [80]. Despite successful training and detection,
limitations included a constrained dataset of diseased broilers due to
disease-related constraints and potential impacts of illumination intensity on
image quality [80].

Barreiros et al. proposed the development of a tracking algorithm using YOLOv2
and Kalman filter to accurately track the movements of groups of zebrafish in
a controlled experimental setup [81]. They successfully implemented a system
that could detect and track individual fish within a group, delimiting the
region of fish heads for detection and estimating the best state of the fish’s
head position in each frame using the Kalman filter

Rančić et al. developed and tested a pipeline for detecting animals,
specifically deer, using YOLOv3, YOLOv4, YOLOv4-tiny, and SSD applied to UAV
images [82]. While the study achieved high-performance predictions,
limitations included the challenge of limited data addressed through pre-
trained models, indicating the need for further research to enhance the
system’s robustness and generalizability [82].

Zheng et al. proposed the YOLO-BYTE algorithm for tracking multiple dairy cows
using a single camera [83]. Despite achieving high precision in dairy cow
target detection, potential environmental impacts on accuracy were
acknowledged, emphasizing the algorithm’s need for further examination across
diverse datasets and scenarios [83].

Wangli, et al. proposed a new pig detection and counting model called
YOLOv5-SA-FC, which integrates shuffle attention and Focal-CIoU loss into the
YOLOv5 framework backbone [84]. By leveraging the shuffle attention module,
the model dynamically focuses on relevant features for pig detection and
counting while reducing the weights of non-essential features. Additionally,
the Focal-CIoU loss mechanism prioritizes predicted boxes with higher overlap
with the target box, improving detection performance. Similarly, Jonggwan et
al. developed an EmbeddedPigCount technology that utilizes TinyYOLOv4 to
accurately count pigs on large-scale pig farms [85]. They collected image data
from a commercial pig farm in Korea, where a surveillance camera captured
images of pigs and humans moving in a hallway. The researchers manually
annotated bounding boxes in the images and used a total of 2675 images for
training the detection module. The research achieved a counting accuracy of
99.44% even when pigs passed through the counting zone back and forth.

###  5.4 Disease Detection in Agriculture Using YOLO

Figure 8: Real-time YOLO-based Leaf Disease Detection

The agricultural sector faces constant challenges in maintaining crop health
and ensuring optimal yields. Identifying and addressing plant diseases
promptly are essential components of sustainable farming practices. With
advancements in computer vision and deep learning, particularly the You Only
Look Once (YOLO) algorithm, there has been a growing interest in leveraging
these technologies for automated disease detection in crops.

This subsection explores several studies that employ YOLO-based models to
detect and monitor diseases in agricultural settings. These studies showcase
the potential of YOLO in providing accurate and efficient solutions for
disease identification, contributing to the development of intelligent and
technology-driven approaches in modern agriculture.

Liu and Wang conducted a study focusing on the detection of tomato diseases
and insect pests in natural environments, culminating in the creation of a
dedicated dataset [86]. Utilizing the YOLO v3 model, they achieved a
commendable detection accuracy of 92.39% in a swift 20.39 ms. The improved
YOLO v3 surpassed alternative methods in both accuracy and speed, including
SSD, Faster R-CNN, and the original YOLO v3. While the algorithm demonstrated
effectiveness in real-time detection, there remains an opportunity to enhance
both accuracy and speed for practical applications, particularly in comparison
to the high precision attainable through deep learning-based classification
methods.

Morbekar and colleagues developed a real-time crop disease detection model
utilizing the YOLO object detection method [87] for detection and
classification, as shown in Figure 8. The model exhibited promising results
with a notable accuracy of 98.5% when tested on the PlantVillage dataset.
However, the study’s limitations include the dataset’s focus on major crops in
India, potentially limiting its applicability to other crops and regions.
Additionally, the system’s scope is confined to detecting diseases solely on
leaves, neglecting other crucial parts of the crop, such as stems or fruits.

Nihar and Raghavendra proposed an innovative approach for real-time rice crop
disease detection, developing a state-of-the-art deep learning model based on
the tiny_yolov3 algorithm [88]. Achieving an impressive accuracy rate of
98.92%, their model facilitates early identification of potential issues,
allowing farmers to proactively safeguard their crops. The authors suggested
the model’s adaptability for pest detection, enhancing its versatility for
diverse applications.

Agbulos et al. employed the YOLO Algorithm to identify rice leaf diseases,
achieving an overall accuracy of 73.33% [89]. While successful in identifying
leaf blast and brown spot diseases, the study’s focus on static rice leaf
images and the hardware setup’s limitations, including the Raspberry Pi 3 and
camera module, pose potential challenges in real-world scenarios. Future
improvements may involve upgrading hardware components for enhanced image
quality and exploring a broader spectrum of diseases in rice plants.

Lippi and colleagues developed an insect detection system using a YOLO-based
Convolutional Neural Network (CNN) for identifying true bugs in hazelnut
orchards [90]. With an average precision of approximately 94.5%, the system
showcased real-time processing capabilities. However, scalability concerns
arise when applying the system to large orchards, and potential challenges
associated with the system’s depth sensor resolution may impact its
performance.

Table 10: Studies on Crop Disease and Pest Detection Using YOLO

Authors |  Model |  Details
---|---|---
[86] |  YOLO v3 |  Detection of tomato diseases and insect pests. Accuracy of 92.39% in 20.39 ms.
[87] |  YOLO |  Real-time crop disease detection. Accuracy of 98.5% on PlantVillage dataset.
[88] |  YOLO-v3 Tiny |  Real-time rice crop disease detection. Accuracy rate of 98.92%.
[89] |  YOLO |  Identification of rice leaf diseases. Overall accuracy of 73.33%.
[90] |  YOLO-based CNN |  Insect detection in hazelnut orchards. Average precision of approximately 94.5%.
[91] |  YOLOv4 |  Mulberry crop disease detection. High speed and accuracy.
[92] |  YOLO V3 |  Apple tree disease detection. Improved accuracy and faster results.
[93] |  YOLO |  Insect detection in soybean crops. High mean average precision.
[94] |  YOLO v5 |  Seed classification in agriculture. Precision and recall of 99%.
[95] |  YOLO v5 |  Bell pepper plant disease identification. Superior accuracy and reduced model size.
[96] |  YOLOv7 |  Tea leaf disease detection. AI-based solution for Bangladesh’s tea cultivation.
[97] |  YOLO-Tea model |  Improved model for tea disease and insect pest detection.

Reddy and Deeksha trained a YOLOv4 model for detecting and identifying leaf
diseases in mulberry crops, achieving high speed and accuracy [91]. The
model’s capability to recommend corresponding pesticides post-detection holds
promise for effective disease management. The study, while successful in its
focus on mulberry crops, encourages further exploration for identifying
various diseases and considering real-time video classification.

Mathew and Mahesh explored the significance of early disease detection in
apple trees, employing YOLO V3 networks for disease detection [92]. The study
highlighted YOLO V3’s benefits, such as faster results and improved accuracy,
but acknowledged challenges, including environmental interference and the need
for continuous plant health monitoring.

Verma et al. proposed a framework employing YOLO algorithms for insect
detection in soybean crops [93]. While achieving high mean average precision,
the study acknowledged limitations, including dataset size constraints and
occasional misclassifications. The potential ethical and environmental
implications of pesticide application based on insect detection were also
noted for further consideration.

Kundu et al. developed a YOLO v5-based system for automated seed segregation
and classification, achieving high precision and recall of 99% [94]. While
impactful in classifying pearl millet and maize seeds, the study identified
the need for further research in seed classification for mixed cropping
scenarios and the broader categorization of seeds based on crop type and
quality.

Mathew and Mahesh employed YOLO v5 for identifying diseases in bell pepper
plants, demonstrating superior accuracy and reduced model size [95]. The study
hinted at the potential extension of disease detection to various diseases
affecting bell pepper plants, promising improvements in farm yield through
prompt disease identification.

Soeb et al. introduced an AI-based solution using the YOLOv7 approach for
detecting tea leaf diseases, emphasizing the need to explore AI’s benefits in
Bangladesh’s tea cultivation [96]. Acknowledging limitations such as limited
labelled data and a lack of established evaluation metrics, the study
advocated for further research to enhance the YOLOv7 model’s effectiveness in
tea leaf disease detection.

Xue et al. proposed the YOLO-Tea model, addressing small target challenges for
tea diseases and insect pests with improved feature extraction and attention
mechanisms [97]. While showcasing its potential through ablative experiments
and comparisons, the study emphasized the need for continued exploration and
evaluation, particularly in real-world tea disease monitoring applications.
Table 10 provides an overview of studies employing various YOLO architectures
for the detection of crop diseases and pests in the agricultural domain.

###  5.5 Precision Framing Using YOLO

Precision farming, a crucial aspect in various agricultural applications,
involves accurately delineating and identifying specific targets within an
image or video frame. The application of You Only Look Once (YOLO) algorithms
in precision framing has demonstrated remarkable capabilities in detecting and
classifying objects with speed and efficiency. This subsection explores
several studies that leverage YOLO for precision framing in diverse
agricultural contexts, showcasing its effectiveness in tasks such as disease
and pest detection, seed classification, and more. These applications not only
enhance the accuracy of target identification but also contribute to
optimizing agricultural processes and improving overall crop management.

Li et al. employed convolutional neural networks (CNNs) to identify
agricultural greenhouses (AGs) in high-resolution satellite images [98]. The
study compared the performance of three prominent CNN-based object detection
models: Faster R-CNN, YOLO v3, and SSD. Utilizing the PyTorch deep learning
framework on a workstation equipped with two TITAN RTX GPUs, the authors
trained and evaluated the models. By fusing GF-1 data into 2 m multispectral
data along with GF-2, the study enhanced sample diversity and assessed method
transferability across distinct data sources exhibiting similar AG styles. The
findings indicated that the YOLO v3 model surpassed the other models in terms
of accuracy and efficiency for AG detection. While the research contributes
valuable insights into AG detection methodologies and CNN-based object
detection in remote sensing, the authors recognized the need for future
investigations to leverage multispectral and hyperspectral data in satellite
images for improved object detection. The study’s exclusive focus on AG
detection prompts further exploration of other geospatial objects in future
research.

Khan et al. developed a deep learning system for distinguishing between crops
and weeds in strawberry and pea fields, aiming for integration into precision
agriculture sprayers for real-time weed management [99]. Unmanned aerial
vehicles equipped with cameras captured field images, and the deep learning
techniques were optimized for high accuracy in identifying small weed patches,
particularly in early growth stages. The system exhibited an overall average
accuracy of 94.73%, outperforming existing machine learning and deep learning-
based approaches. Despite its robustness, the study acknowledged limitations
related to dataset size, generalizability, and real-time integration into
precision spraying systems, calling for additional research and development.

Mamdouh and Khattab introduced a YOLO-based deep learning framework for
detecting and counting olive fruit flies in orchards [100]. The framework
demonstrated exceptional precision (0.84), recall (0.97), F1-score (0.9), and
mean Average Precision (mAP) of 96.68%, surpassing existing pest detection
systems. The authors highlighted the framework’s potential benefits over
traditional manual methods, emphasizing its effectiveness through extensive
simulation experiments. Recognizing the framework’s limitations, including a
lack of a large-scale dataset, the authors proposed future improvements such
as real-life image evaluation, dataset enrichment, and transformation into a
multi-class classifier.

###  5.6 Comparative Analysis

Our methodology systematically compares different YOLO applications within
agricultural settings, integrating key aspects such as task complexity,
control over experimental conditions, hardware dependencies, and a critical
analysis of results and error measurement methods. This holistic approach not
only assesses performance but also elucidates the practical implications of
deploying these models in real-world agricultural scenarios.

In the comparative analysis of different YOLO versions across various
application domains, particularly in agriculture, each version of the YOLO
architecture demonstrates unique strengths and encounters specific limitations
that influence its suitability for certain tasks (see Table 11). For instance,
YOLOv1 primarily addressed standard datasets like VOC 2007 or Sesame Fields
Image Dataset with straightforward object recognition tasks like weed
detection. In contrast, later versions like YOLOv4 and YOLOv5 have been
utilized in more complex agricultural datasets that feature varied
backgrounds, multiple object classes, and demand real-time detection
capabilities. These tasks are evaluated not only for accuracy but also for the
model’s ability to manage the intricacies of natural scenes, including
variable lighting conditions, occlusions, and overlapping objects.

Our analysis underscores the importance of controlling experimental conditions
to validate the robustness of object detection models. YOLOv3’s deployment in
UAV-based weed detection, for instance, involves not just the algorithm’s
performance but also factors like flight stability, camera quality, and
environmental interference—all of which significantly impact outcomes.
Similarly, YOLOv6’s application in wildlife monitoring presents challenges
such as varying animal speeds and camouflaged backgrounds, pushing the limits
of detection capabilities under less controlled but highly variable
conditions. The choice of hardware significantly influences the deployment of
YOLO models. Our review spans hardware from high-end GPUs for training to
embedded systems like NVIDIA Jetson for infield deployment, critically
evaluating performance metrics like frame rate, processing speed, and power
consumption to determine feasibility in agricultural settings where limited
power and mobility often dictate hardware choices.

Our methodology includes a critical review of error measurement techniques,
now detailed in the ’Performance Metrics’ column of our comparative analysis
table (see Table 11). We focus on metrics such as precision, recall, mAP, and
F1 score, which are crucial for assessing model performance across different
scenarios. Additionally, we categorize errors into types such as localization,
classification, and false positives/negatives. This categorization not only
provides a nuanced view of model performance but also enhances our
understanding of the practical implications of deploying these models in field
conditions. For instance, in YOLOv4’s application in crop disease detection,
understanding the impact of false negatives is crucial, as missing a diseased
plant could lead to wider spread within the crop.

Compiling data from this comprehensive methodology allows us to synthesize
findings across different YOLO applications and versions, highlighting trends
like improvements in speed and accuracy versus trade-offs in computational
demand and complexity. This synthesis not only addresses current research
questions but also identifies gaps for future research, suggesting potential
model training or deployment enhancements to better meet the specific needs of
precision agriculture. The evolution of the YOLO architectures marks a
significant trajectory of technological enhancements, methodically refined to
address the diverse and challenging demands of agricultural applications. Each
version from YOLOv1 through YOLOv10 has been adapted to overcome specific
limitations, leading to more sophisticated systems capable of complex
environmental interactions. These iterative advancements highlight the
importance of selecting the appropriate YOLO variant tailored to specific
agricultural tasks, balancing computational demands with precision
requirements for tasks such as pest identification and crop disease
monitoring. By aligning the model capabilities with task-specific needs,
researchers and practitioners can harness YOLO technologies to propel the
future of precision agriculture and sustainable farming practices effectively.

This comparative analysis highlights the critical role of model selection
based on specific application needs and the technological trade-offs involved,
providing a framework for future research and application in technology-driven
agriculture.

Table 11: Comparative Analysis of Different YOLO Versions Across Various Application Domains YOLO Version |  Application Domain |  Strengths |  Limitations
---|---|---|---
YOLOv1 |  Object Detection, Weed and Crop Detection [63, 68, 69, 76, 87, 89, 92, 93] |  Fast real-time processing |  Struggles with small objects
YOLOv2 |  Animal Tracking [81] |  Improved recall, handles small objects better |  Higher computational requirements
YOLOv3 |  Agriculture [61, 70, 71, 72, 77, 86, 88, 92] |  Multi-scale detection, good in diverse conditions |  Not optimized for very low-power devices
YOLOv4 |  Agriculture [62, 66, 75, 78, 91, 85] |  Robust in complex visual environments |  Setup complexity for training on custom datasets
YOLOv5 |  Weed Detection [65, 67, 94, 95, 84] |  Very fast, suitable for real-time applications |  May require fine-tuning for specific scenarios
YOLOv6 |  Wildlife Monitoring [101] |  High accuracy with enhanced depth |  Needs high computation power for best results
YOLOv7 |  Crop Disease Detection [96] |  High precision, effective in crowded scenes |  Computational efficiency decreases with scale
YOLOv8 |  Agriculture [80, 102] |  Very high speed, suitable for dynamic environments |  Can struggle with very small or fast-moving objects
YOLOv9 |  Plant Disease detection [103, 104] |  High accuracy and recall, suitable for detailed medical scans |  Requires extensive dataset for training
YOLOv10 |  — |  Enhanced accuracy-efficiency trade-off, NMS-free model |  Complex configuration for multi-source integration

##  6 Discussion

The integration of YOLO variants in agriculture has emerged as a
transformative approach, revolutionizing various aspects of farming and crop
management. As evidenced in Table 8, 9 and 10, the diverse range of YOLO
models employed for agricultural applications showcases the adaptability and
efficacy of this architecture in addressing the unique challenges within the
agricultural domain. Notably, the consistently high levels of accuracy
achieved across different applications, including weed detection, crop
identification, and disease diagnosis, underscore the robust performance of
YOLO-based models in diverse agricultural scenarios.

Real-time Precision Agriculture: One of the standout features of YOLO variants
in agriculture is their ability to facilitate real-time precision farming. The
models, such as YOLOv3, YOLOv4, and YOLOv5, have demonstrated exceptional
swiftness and accuracy in detecting and identifying objects in agricultural
landscapes. This real-time capability holds significant promise for optimizing
farming practices, enabling timely decision-making, and enhancing resource
allocation efficiency.

Weed Detection and Management: The application of YOLO variants in weed
detection, as illustrated by studies such as [61, 62, 63, 64, 65, 66, 67, 68,
69, 70], signifies a paradigm shift in traditional weed management practices.
The real-time identification and localization of weeds using YOLO models
empower farmers to implement targeted and efficient weed control measures.
Despite certain challenges, such as resolution limitations in detecting
smaller objects, the overall performance of YOLO-based systems like YOLO-WEED
[61] and YOLOv4-weeds [62] indicates their potential for widespread adoption
in weed management.

Crop Detection and Monitoring: The significance of robust crop detection
methodologies in precision agriculture cannot be overstated. YOLO variants, as
exemplified by studies such as [71, 72, 73, 74, 75, 76], contribute to
accurate identification and delineation of crops in large-scale fields. These
models offer a comprehensive solution for monitoring crop health, estimating
yields, and optimizing resource management. The development of specialized
models like CitrusYOLO [74] and YOLO-P [73] for specific crops further
emphasizes the adaptability of YOLO architectures to diverse agricultural
settings.

##  7 Challenges in YOLO-based Agricultural Applications

While the achievements in agricultural applications of YOLO are remarkable,
challenges persist. The regional specificity in some datasets, hardware
constraints, and the need for further research to enhance model applicability
are areas that warrant attention. Future work should focus on addressing these
challenges, exploring the generalizability of models to different crops and
environmental conditions, and fostering advancements in sensing technologies
for broader applications.

###  7.1 Data Specificity and Generalization

One notable challenge in YOLO-based agricultural applications lies in the
specificity of datasets used for model training. Many studies focus on
particular crops or regions, which may limit the model’s generalizability
across diverse agricultural landscapes [105]. Addressing this challenge
involves creating more comprehensive and diverse datasets that encompass
various crops, growth stages, and environmental conditions [106].
Additionally, research efforts should aim at developing transfer learning
techniques to enhance model adaptability to new agricultural contexts [107].

###  7.2 Hardware Limitations

The deployment of YOLO-based systems in real-world agricultural settings may
encounter hardware constraints, especially in resource-limited environments
[108]. Many studies leverage powerful computing resources for model training
and inference, but practical implementation on edge devices or embedded
systems poses challenges [109]. Future research should explore model
optimization techniques, quantization, and lightweight architectures to make
YOLO variants more accessible for deployment on edge devices commonly used in
precision farming equipment.

###  7.3 Environmental Variability

Agricultural environments are inherently dynamic, with variations in lighting
conditions, weather, and terrain. YOLO models, while robust, may face
challenges in adapting to these environmental changes. Ensuring the
reliability of detection under diverse conditions requires the development of
models that are resilient to variations in illumination, adverse weather, and
different terrains. This necessitates the incorporation of environmental
adaptability in model training and further exploration of techniques like
domain adaptation.

###  7.4 Small Object Detection

Identifying diminutive weeds or diseases in agricultural contexts presents a
unique set of hurdles for YOLO variants. The intrinsic structure of YOLO may
encounter difficulties in discerning smaller objects within an image.
Addressing this obstacle necessitates advancements in feature extraction,
attention mechanisms, or the exploration of multi-scale detection strategies.
Moreover, ensemble techniques can be used to overcome false positives and
negatives as done by researchers [110, 111, 112, 113]. It is crucial for
future research to concentrate on refining YOLO architectures to augment the
precision of detecting small objects in precision farming applications.
Furthermore, the integration of attention mechanisms can serve as a valuable
approach, steering YOLO architectures towards subtle defects, as has been
successfully implemented in industries like textiles [114, 115, 116, 117, 118,
119].

##  8 Future Directions and Opportunities

###  8.1 Multi-Modal Integration

The integration of multi-modal data sources, such as combining RGB images with
thermal or hyperspectral data, holds great potential for advancing YOLO-based
agricultural applications [120]. Combining different modalities can provide
richer information for more accurate and robust detection of crops, weeds, and
diseases. Future research should explore the fusion of diverse data sources to
enhance the overall performance and reliability of YOLO models in precision
agriculture.

###  8.2 Explainability and Interpretability

As YOLO models become integral to decision-making in agriculture, ensuring
their explainability and interpretability is crucial. Farmers and stakeholders
need to understand the rationale behind model predictions to trust and
effectively implement precision farming practices. Future work should focus on
developing methodologies for explaining YOLO model decisions and providing
insights into how and why certain detections are made, especially in complex
and dynamic agricultural environments.

###  8.3 Real-Time Adaptive Systems

The evolution of YOLO architectures toward real-time capabilities opens
avenues for developing adaptive systems that respond dynamically to changing
agricultural conditions. Future YOLO-based models could incorporate real-time
learning mechanisms, enabling them to adapt and improve their performance over
time based on continuous feedback from the field. This would contribute to the
development of intelligent and self-improving precision agriculture systems.

###  8.4 Human-AI Collaboration

Recognizing the expertise of farmers, future research should explore models
that facilitate human-AI collaboration in decision-making processes.
Integrating farmer knowledge with AI-driven insights can lead to more
effective and context-aware agricultural practices. Human-AI collaboration is
vital for addressing the complexities and uncertainties inherent in
agriculture, allowing for seamless integration of YOLO-based technologies into
the existing farming ecosystem.

In summary, overcoming the challenges and leveraging future opportunities
requires a concerted effort from the research community, industry
stakeholders, and farmers. The continuous refinement of YOLO-based models,
coupled with advancements in data collection, hardware, and interpretability,
will propel the application of AI in agriculture towards sustainable and
efficient farming practices.

##  9 Conclusion

In conclusion, the intersection of YOLO variants and agriculture presents a
transformative potential for precision farming, weed management, and crop
monitoring. The consistent advancements and promising results showcased in
various studies underscore the pivotal role of YOLO architectures in shaping
the future of smart and efficient agriculture. The comprehensive review of
YOLO variants in agricultural applications highlights the transformative
potential of these models in revolutionizing precision farming. From crop
detection and disease identification to weed management, YOLO variants have
showcased remarkable capabilities, offering real-time and efficient solutions
to long-standing challenges in agriculture. The discussion and analysis of
various studies underscore the versatility and adaptability of YOLO
architectures across diverse agricultural scenarios. Despite the evident
successes, challenges persist, necessitating ongoing research efforts. The
specificity of training datasets, hardware limitations, and environmental
variability pose hurdles that demand innovative solutions. Future research
should prioritize the development of more inclusive datasets, optimization
techniques for edge devices, and models resilient to dynamic agricultural
environments.

Looking ahead, multi-modal integration, explainability, and real-time adaptive
systems present exciting opportunities for further enhancing the utility of
YOLO models in agriculture. The fusion of different data modalities, coupled
with real-time learning mechanisms, can usher in a new era of intelligent and
context-aware precision farming. Additionally, a focus on human-AI
collaboration acknowledges the indispensable role of farmers in the decision-
making process, promoting a harmonious integration of AI technologies into
existing agricultural practices. In conclusion, the evolution of YOLO variants
in agriculture signifies a paradigm shift towards sustainable, efficient, and
technologically-driven farming practices. As researchers, practitioners, and
stakeholders collaborate, the future holds great promise for the continued
advancement of YOLO-based applications, contributing to the global effort to
address food security and promote environmentally conscious agriculture.

## References

  * [1] Diwan P Ariana, Renfu Lu, and Daniel E Guyer.  Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers.  Computers and electronics in agriculture, 53(1):60–70, 2006.
  * [2] Muhammad Hussain.  Yolo-v1 to yolo-v8, the rise of yolo and its complementary nature toward digital manufacturing and industrial defect detection.  Machines, 11(7):677, 2023.
  * [3] Xiaolin Zhu and Guanghui Li.  Rapid detection and visualization of slight bruise on apples using hyperspectral imaging.  International journal of food properties, 22(1):1709–1719, 2019.
  * [4] Amirhossein Zaji, Zheng Liu, Gaozhi Xiao, Pankaj Bhowmik, Jatinder S Sangha, and Yuefeng Ruan.  Wheat spikes height estimation using stereo cameras.  IEEE Transactions on AgriFood Electronics, 2023.
  * [5] Shuang-Lin Mao, Yu-Ming Wei, Wenguang Cao, Xiu-Jin Lan, Ma Yu, Zheng-Mao Chen, Guo-Yue Chen, and You-Liang Zheng.  Confirmation of the relationship between plant height and fusarium head blight resistance in wheat (triticum aestivum l.) by qtl meta-analysis.  Euphytica, 174:343–356, 2010.
  * [6] Xu Wang, Daljit Singh, Sandeep Marla, Geoffrey Morris, and Jesse Poland.  Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies.  Plant Methods, 14(1):1–16, 2018.
  * [7] S. Liao, J. Wang, R. Yu, K. Sato, and Z. Cheng.  Cnn for situations understanding based on sentiment analysis of twitter data.  Procedia Computer Science, 111:376–381, 2017.
  * [8] H. Sak, A. Senior, K. Rao, and F. Beaufays.  Fast and accurate recurrent neural network acoustic models for speech recognition.  arXiv:1507.06947 [cs, stat], Jul. 2015.
  * [9] Mujadded Al Rabbani Alif, Sabbir Ahmed, and Muhammad Abul Hasan.  Isolated bangla handwritten character recognition with convolutional neural network.  pages 1–6, 2017.
  * [10] Y. Zhang, Y. Tong, and Y. Jiang.  Study of sentiment classification for chinese microblog based on recurrent neural network.  Chinese Journal of Electronics, 25(4):601–607, Jul. 2016.
  * [11] S. Lai, L. Xu, K. Liu, and J. Zhao.  Recurrent convolutional neural networks for text classification.  Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), Feb. 2015.
  * [12] D. Wei and et al.  Research on unstructured text data mining and fault classification based on rnn-lstm with malfunction inspection report.  Energies, 10(3):406, Mar. 2017.
  * [13] D. Quang and X. Xie.  Danq: A hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences.  Nucleic Acids Research, 44(11):e107–e107, Apr. 2016.
  * [14] Mujadded Al Rabbani Alif, Muhammad Hussain, Gareth Tucker, and Simon Iwnicki.  Boltvision: A comparative analysis of cnn, cct, and vit in achieving high accuracy for missing bolt classification in train components.  Machines, 12(2):93, 2024.
  * [15] Mujadded Al Rabbani Alif and Muhammad Hussain.  Lightweight convolutional network with integrated attention mechanism for missing bolt detection in railways.  Metrology, 4(2):254–278, 2024.
  * [16] Mujadded Al Rabbani Alif.  Attention-based automated pallet racking damage detection.  9(1), 2024.
  * [17] M. R. Mezaal, B. Pradhan, M. I. Sameen, H. Z. Mohd Shafri, and Z. M. Yusoff.  Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data.  Applied Sciences, 7(7):730, Jul. 2017.
  * [18] N. Xu and et al.  Dual-stream recurrent neural network for video captioning.  IEEE Transactions on Circuits and Systems for Video Technology, 29(8):2482–2493, Aug. 2019.
  * [19] J. Kim, J. Kim, H. L. Thu, and H. Kim.  Long short term memory recurrent neural network classifier for intrusion detection.  In 2016 International Conference on Platform Technology and Service (PlatCon), 2016.
  * [20] A. M. Rather, A. Agarwal, and V. N. Sastry.  Recurrent neural network and a hybrid model for prediction of stock returns.  Expert Systems with Applications, 42(6):3234–3241, Apr. 2015.
  * [21] Ming Liang and Xiaolin Hu.  Recurrent convolutional neural network for object recognition.  IEEE Xplore, Jun. 2015.  (accessed Dec. 03, 2020).
  * [22] W. Nash, T. Drummond, and N. Birbilis.  A review of deep learning in the study of materials degradation.  npj Materials Degradation, 2(1), Nov. 2018.
  * [23] T. Diwan, G. Anirudh, and J. V. Tembhurne.  Object detection using yolo: Challenges, architectural successors, datasets and applications.  Multimedia Tools and Applications, Aug. 2022.
  * [24] Y. Bengio, A. Courville, and P. Vincent.  Unsupervised feature learning and deep learning: A review and new perspectives, 2012.  Accessed: May 17, 2023.
  * [25] Ujjwal.  An intuitive explanation of convolutional neural networks.  the data science blog, May 29 2017.
  * [26] K. Chen, K. Franko, and R. Sang.  Structured model pruning of convolutional networks on tensor processing units.  arXiv:2107.04191 [cs], Jul. 2021.
  * [27] S. Agarwal, J. O. D. Terrail, and F. Jurie.  Recent advances in object detection in the age of deep convolutional neural networks.  arXiv.org, Aug. 20 2019.
  * [28] L. Liu and et al.  Deep learning for generic object detection: A survey.  Sep. 2018.
  * [29] C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh.  Cspnet: A new backbone that can enhance learning capability of cnn.  openaccess.thecvf.com, 2020.  (accessed Apr. 23, 2023).
  * [30] Xingxing Xie, Gong Cheng, Jiabao Wang, Xiwen Yao, and Junwei Han.  Oriented r-cnn for object detection.  In Proceedings of the IEEE/CVF international conference on computer vision, pages 3520–3529, 2021.
  * [31] R. Girshick.  Fast r-cnn.  In International Conference on Computer Vision, pages 1137–1149, 2015.
  * [32] S. Ren and et al.  Faster r-cnn: towards real-time object detection with region proposal networks.  In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1137–1149, 2017.
  * [33] T.-Y. Lin and et al.  Feature pyramid networks for object detection.  In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 936–944, 2017.
  * [34] W. Liu and et al.  Ssd: single shot multibox detector.  In European Conference on Computer Vision, 2016.
  * [35] Chang Sun, Yibo Ai, Sheng Wang, and Weidong Zhang.  Dense-refinedet for traffic sign detection and classification.  Sensors, 20(22):6570, 2020.
  * [36] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and Piotr Dollár.  Focal loss for dense object detection.  arXiv (Cornell University), Aug. 2017.
  * [37] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi.  You only look once: Unified, real-time object detection.  In CVPR, pages 779–788, 2016.
  * [38] J. Redmon and A. Farhadi.  Yolo9000: Better, faster, stronger.  Dec. 2016.
  * [39] F. Borisyuk, A. Gordo, and V. Sivakumar.  Rosetta.  In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2018.
  * [40] J.-H. Won, D.-H. Lee, K.-M. Lee, and C.-H. Lin.  An improved yolov3-based neural network for de-identification technology.  IEEE Xplore, Jun. 2019.
  * [41] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman.  The PASCAL visual object classes (voc) challenge.  International Journal of Computer Vision, 88(2):303–338, 2010.
  * [42] T.-Y. Lin et al.  Microsoft coco: Common objects in context.  2014\.
  * [43] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao.  Yolov4: Optimal speed and accuracy of object detection.  Apr. 2020.
  * [44] K. He, X. Zhang, S. Ren, and J. Sun.  Deep residual learning for image recognition.  openaccess.thecvf.com, 2016.
  * [45] Z. Ma, M. Li, and Y. Wang.  Pan: Path integral based convolution for deep graph neural networks.  Apr. 2019.
  * [46] Z. Yao, Y. Cao, S. Zheng, G. Huang, and S. Lin.  Cross-iteration batch normalization.  In openaccess.thecvf.com, 2021.
  * [47] S. He, R. Bao, J. Li, P. E. Grant, and Y. Ou.  Accuracy of segment-anything model (sam) in medical image segmentation tasks.  Apr. 2023.
  * [48] J. Solawetz.  YOLOv5 New Version - Improvements and Evaluation.  Roboflow Blog, Jun. 2020.
  * [49] C.-Y. Wang, A. Bochkovskiy, and H.-Y. Liao.  YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, Jul. 2022.
  * [50] Z. Wang et al.  Mosaic representation learning for self-supervised visual pre-training.  openreview.net, Feb. 2023.
  * [51] J. Solawetz and et al.  What’s new in yolov6?, Jul. 2022.
  * [52] X. Xu, Y. Jiang, W. Chen, Y. Huang, Y. Zhang, and X. Sun.  Damo-yolo: A report on real-time object detection design, Dec. 2022.
  * [53] X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun.  Repvgg: Making vgg-style convnets great again.  In openaccess.thecvf.com, 2021.
  * [54] G. Huang, Z. Liu, and Kilian Q Weinberger.  Densely connected convolutional networks.  2016\.
  * [55] G. Jocher, A. Chaurasia, and J. Qiu.  Yolo by ultralytics, 2023.  Accessed: February 30, 2023.
  * [56] J. Solawetz.  Yolov7 - a breakdown of how it works, Jul. 2022.
  * [57] J. Solawetz and et al.  What is yolov8? the ultimate guide, Jan. 2023.
  * [58] G. Jocher and et al.  ultralytics/yolov5: v3.0, Aug. 2020.
  * [59] Chien-Yao Wang and Hong-Yuan Mark Liao.  YOLOv9: Learning what you want to learn using programmable gradient information.  2024\.
  * [60] Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding.  Yolov10: Real-time end-to-end object detection.  arXiv preprint arXiv:2405.14458, 2024.
  * [61] Addie Ira Borja PARICO and Tofael AHAMED.  An aerial weed detection system for green onion crops using the you only look once (yolov3) deep learning algorithm.  Engineering in Agriculture, Environment and Food, 13(2):42–48, 2020.
  * [62] Boyu Ying, Yuancheng Xu, Shuai Zhang, Yinggang Shi, and Li Liu.  Weed detection in images of carrot fields based on improved yolo v4.  Traitement du Signal, 38(2), 2021.
  * [63] Mads Dyrmann, Anders Krogh Mortensen, Lars Linneberg, Toke Thomas Høye, and Kim Bjerge.  Camera assisted roadside monitoring for invasive alien plant species using deep learning.  Sensors, 21(18):6126, 2021.
  * [64] Jiqing Chen, Huabin Wang, Hongdu Zhang, Tian Luo, Depeng Wei, Teng Long, and Zhikui Wang.  Weed detection in sesame fields using a yolo model with an enhanced attention mechanism and feature fusion.  Computers and Electronics in Agriculture, 202:107412, 2022.
  * [65] Qifan Wang, Man Cheng, Shuo Huang, Zhenjiang Cai, Jinlin Zhang, and Hongbo Yuan.  A deep learning approach incorporating yolo v5 and attention mechanisms for field real-time detection of the invasive weed solanum rostratum dunal seedlings.  Computers and Electronics in Agriculture, 199:107194, 2022.
  * [66] Benjamin Costello, Olusegun O Osunkoya, Juan Sandino, William Marinic, Peter Trotter, Boyang Shi, Felipe Gonzalez, and Kunjithapatham Dhileepan.  Detection of parthenium weed (parthenium hysterophorus l.) and its growth stages using artificial intelligence.  Agriculture, 12(11):1838, 2022.
  * [67] Fengying Dang, Dong Chen, Yuzhen Lu, and Zhaojian Li.  Yoloweeds: a novel benchmark of yolo object detectors for multi-class weed detection in cotton production systems.  Computers and Electronics in Agriculture, 205:107655, 2023.
  * [68] Fernando J Pérez-Porras, Jorge Torres-Sánchez, Francisca López-Granados, and Francisco J Mesas-Carrascosa.  Early and on-ground image-based detection of poppy (papaver rhoeas) in wheat using yolo architectures.  Weed Science, 71(1):50–58, 2023.
  * [69] Mino Sportelli, Orly Enrique Apolo-Apolo, Marco Fontanelli, Christian Frasconi, Michele Raffaelli, Andrea Peruzzi, and Manuel Perez-Ruiz.  Evaluation of yolo object detectors for weed detection in different turfgrass scenarios.  Applied Sciences, 13(14):8502, 2023.
  * [70] Xiaojun Jin, Yanxia Sun, Jun Che, Muthukumar Bagavathiannan, Jialin Yu, and Yong Chen.  A novel deep learning-based method for detection of weeds in vegetables.  Pest Management Science, 78(5):1861–1869, 2022.
  * [71] Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, and Zize Liang.  Apple detection during different growth stages in orchards using the improved yolo-v3 model.  Computers and electronics in agriculture, 157:417–426, 2019.
  * [72] Shaun M Sharpe, Arnold W Schumann, and Nathan S Boyd.  Goosegrass detection in strawberry and tomato using a convolutional neural network.  Scientific Reports, 10(1):9548, 2020.
  * [73] Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Subbiah Thannirmalai, and Mahidzal Dahari.  An optimized yolo-based object detection model for crop harvesting system.  IET Image Processing, 15(9):2112–2125, 2021.
  * [74] Wenkang Chen, Shenglian Lu, Binghao Liu, Ming Chen, Guo Li, and Tingting Qian.  Citrusyolo: A algorithm for citrus detection under orchard environment based on yolov4.  Multimedia Tools and Applications, 81(22):31363–31389, 2022.
  * [75] Qingqing Hong, Ling Jiang, Zhenghua Zhang, Shu Ji, Chen Gu, Wei Mao, Wenxi Li, Tao Liu, Bin Li, and Changwei Tan.  A lightweight model for wheat ear fusarium head blight detection based on rgb images.  Remote Sensing, 14(14):3481, 2022.
  * [76] Liang Wang, Lingmin Li, Hao Wang, Shaohua Zhu, Zhiqiang Zhai, and Zhongxiang Zhu.  Real-time vehicle identification and tracking during agricultural master-slave follow-up operation using improved yolo v4 and binocular positioning.  Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 237(6):1393–1404, 2023.
  * [77] Juan Wang, Nan Wang, Lihua Li, and Zhenhui Ren.  Real-time behavior detection and judgment of egg breeders based on yolo v3.  Neural Computing and Applications, 32:5471–5481, 2020.
  * [78] Anne K Schütz, Verena Schöler, E Tobias Krause, Mareike Fischer, Thomas Müller, Conrad M Freuling, Franz J Conraths, Mario Stanke, Timo Homeier-Bachmann, and Hartmut HK Lentz.  Application of yolov4 for detection and motion monitoring of red foxes.  Animals, 11(6):1723, 2021.
  * [79] Zhenwei Yu, Yuehua Liu, Sufang Yu, Ruixue Wang, Zhanhua Song, Yinfa Yan, Fade Li, Zhonghua Wang, and Fuyang Tian.  Automatic detection method of dairy cow feeding behaviour based on yolo improved model and edge computing.  Sensors, 22(9):3271, 2022.
  * [80] Wael M Elmessery, Joaquín Gutiérrez, Gomaa G Abd El-Wahhab, Ibrahim A Elkhaiat, Ibrahim S El-Soaly, Sadeq K Alhag, Laila A Al-Shuraym, Mohamed A Akela, Farahat S Moghanm, and Mohamed F Abdelshafie.  Yolo-based model for automatic detection of broiler pathological phenomena through visual and thermal images in intensive poultry houses.  Agriculture, 13(8):1527, 2023.
  * [81] Marta de Oliveira Barreiros, Diego de Oliveira Dantas, Luís Claudio de Oliveira Silva, Sidarta Ribeiro, and Allan Kardec Barros.  Zebrafish tracking using yolov2 and kalman filter.  Scientific reports, 11(1):3219, 2021.
  * [82] Kristina Rančić, Boško Blagojević, Atila Bezdan, Bojana Ivošević, Bojan Tubić, Milica Vranešević, Branislav Pejak, Vladimir Crnojević, and Oskar Marko.  Animal detection and counting from uav images using convolutional neural networks.  Drones, 7(3):179, 2023.
  * [83] Zhiyang Zheng, Jingwen Li, and Lifeng Qin.  Yolo-byte: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows.  Computers and Electronics in Agriculture, 209:107857, 2023.
  * [84] Wangli Hao, Li Zhang, Meng Han, Kai Zhang, Fuzhong Li, Guoqiang Yang, and Zhenyu Liu.  Yolov5-sa-fc: A novel pig detection and counting method based on shuffle attention and focal complete intersection over union.  Animals, 13(20):3201, 2023.
  * [85] Jonggwan Kim, Yooil Suh, Junhee Lee, Heechan Chae, Hanse Ahn, Yongwha Chung, and Daihee Park.  Embeddedpigcount: Pig counting with video object detection and tracking on an embedded board.  Sensors, 22(7):2689, 2022.
  * [86] Jun Liu and Xuewei Wang.  Tomato diseases and pests detection based on improved yolo v3 convolutional neural network.  Frontiers in plant science, 11:898, 2020.
  * [87] Achyut Morbekar, Ashi Parihar, and Rashmi Jadhav.  Crop disease detection using yolo.  In 2020 International Conference for Emerging Technology (INCET), pages 1–5, 2020.
  * [88] G Nihar, V Raghavendra, V Suresh, and M Sandhya.  Rice crop disease detection using yolo algorithm.  In National Conference On Advances in Electronics Signal Processing and Communications (AESPC-2020), volume 6, 2020.
  * [89] Ma Kristin Agbulos, Yovito Sarmiento, and Jocelyn Villaverde.  Identification of leaf blast and brown spot diseases on rice leaf with yolo algorithm.  In 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE), pages 307–312. IEEE, 2021.
  * [90] Martina Lippi, Niccolò Bonucci, Renzo Fabrizio Carpio, Mario Contarini, Stefano Speranza, and Andrea Gasparri.  A yolo-based pest detection system for precision agriculture.  In 2021 29th Mediterranean Conference on Control and Automation (MED), pages 342–347, 2021.
  * [91] Monalika Padma Reddy and A Deeksha.  Mulberry leaf disease detection using yolo.  International Journal of Advance Research, Ideas and Innovations in Technology, 7:1816–1821, 2021.
  * [92] Midhun P Mathew and Therese Yamuna Mahesh.  Determining the region of apple leaf affected by disease using yolo v3.  In 2021 International Conference on Communication, Control and Information Sciences (ICCISc), volume 1, pages 1–4, 2021.
  * [93] Shani Verma, Shrivishal Tripathi, Anurag Singh, Muneendra Ojha, and Ravi R Saxena.  Insect detection and identification using yolo algorithms on soybean crop.  In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), pages 272–277, 2021.
  * [94] Nidhi Kundu, Geeta Rani, and Vijaypal Singh Dhaka.  Seeds classification and quality testing using deep learning and yolo v5.  In Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, pages 153–160, 2021.
  * [95] Midhun P Mathew and Therese Yamuna Mahesh.  Leaf-based disease detection in bell pepper plant using yolo v5.  Signal, Image and Video Processing, pages 1–7, 2022.
  * [96] Md Janibul Alam Soeb, Md Fahad Jubayer, Tahmina Akanjee Tarin, Muhammad Rashed Al Mamun, Fahim Mahafuz Ruhad, Aney Parven, Nabisab Mujawar Mubarak, Soni Lanka Karri, and Islam Md Meftaul.  Tea leaf disease detection and identification based on yolov7 (yolo-t).  Scientific reports, 13(1):6078, 2023.
  * [97] Zhenyang Xue, Renjie Xu, Di Bai, and Haifeng Lin.  Yolo-tea: A tea disease detection model improved by yolov5.  Forests, 14(2):415, 2023.
  * [98] Min Li, Zhijie Zhang, Liping Lei, Xiaofan Wang, and Xudong Guo.  Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster r-cnn, yolo v3 and ssd.  Sensors, 20(17):4938, 2020.
  * [99] Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmad Khan, and Shahzad Anwar.  Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer.  Precision Agriculture, 22(6):1711–1727, 2021.
  * [100] Nariman Mamdouh and Ahmed Khattab.  Yolo-based deep learning framework for olive fruit fly detection and counting.  IEEE Access, 9:84252–84262, 2021.
  * [101] Wenjie Mao, Gang Li, and Xiaowei Li.  Improved re-parameterized convolution for wildlife detection in neighboring regions of southwest china.  Animals, 14(8):1152, 2024.
  * [102] Jiayou Shi, Yuhao Bai, Jun Zhou, and Baohua Zhang.  Multi-crop navigation line extraction based on improved yolo-v8 and threshold-dbscan under complex agricultural environments.  Agriculture, 14(1):45, 2023.
  * [103] Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou, and Jen-Shiun Chiang.  Yolov9 for fracture detection in pediatric wrist trauma x-ray images.  arXiv preprint arXiv:2403.11249, 2024.
  * [104] Boudjemaa Boudaa, Kamel Abada, Walid Aymen Aichouche, and Ahmed Nabil Belakermi.  Advancing plant diseases detection with pre-trained yolo models.  In 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS), pages 1–6. IEEE, 2024.
  * [105] Zolo Kiala, John Odindi, and Onisimo Mutanga.  Determining the capability of the tree-based pipeline optimization tool (tpot) in mapping parthenium weed using multi-date sentinel-2 image data.  Remote Sensing, 14(7):1687, 2022.
  * [106] Wen-Hao Su.  Advanced machine learning in point spectroscopy, rgb-and hyperspectral-imaging for automatic discriminations of crops and weeds: A review.  Smart Cities, 3(3):767–792, 2020.
  * [107] Yuri Shendryk, Natalie A Rossiter-Rachor, Samantha A Setterfield, and Shaun R Levick.  Leveraging high-resolution satellite imagery and gradient boosting for invasive weed mapping.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:4443–4450, 2020.
  * [108] Arsalan Zahid, Muhammad Hussain, Richard Hill, and Hussain Al-Aqrabi.  Lightweight convolutional network for automated photovoltaic defect detection.  In 2023 9th International Conference on Information Technology Trends (ITT), pages 133–138. IEEE, 2023.
  * [109] Muhammad Hussain.  Exudate detection: Integrating retinal-based affine mapping and design flow mechanism to develop lightweight architectures.  IEEE Access, 2023.
  * [110] Hanse Ahn, Seungwook Son, Heegon Kim, Sungju Lee, Yongwha Chung, and Daihee Park.  Ensemblepigdet: Ensemble deep learning for accurate pig detection.  Applied Sciences, 11(12):5577, 2021.
  * [111] Manzhou Li, Siyu Cheng, Jingyi Cui, Changxiang Li, Zeyu Li, Chang Zhou, and Chunli Lv.  High-performance plant pest and disease detection based on model ensemble with inception module and cluster algorithm.  Plants, 12(1):200, 2023.
  * [112] Sangyeon Lee, Amarpreet Singh Arora, and Choa Mun Yun.  Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model.  Frontiers in Plant Science, 13:991134, 2022.
  * [113] Priya Singh and Rajalakshmi Krishnamurthi.  Object detection using deep ensemble model for enhancing security towards sustainable agriculture.  International Journal of Information Technology, 15(6):3113–3126, 2023.
  * [114] Rui Jin and Qiang Niu.  Automatic fabric defect detection based on an improved yolov5.  Mathematical Problems in Engineering, 2021, 2021.
  * [115] Jiaqi Zhang, Junfeng Jing, Pengwen Lu, and Shaojun Song.  Improved mobilenetv2-ssdlite for automatic fabric defect detection system based on cloud-edge computing.  Measurement, 201:111665, 2022.
  * [116] Bing Wei, Bailing Xu, Kuangrong Hao, and Lei Gao.  Textile defect detection using multilevel and attentional deep learning network (mlma-net).  Textile Research Journal, 92:004051752110737, 02 2022.
  * [117] Zhengrui Peng, Xinyi Gong, Zhenfeng Lu, Xiangyi Xu, Bengang Wei, and Mukesh Prasad.  A novel fabric defect detection network based on attention mechanism and multi-task fusion.  In 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), pages 484–488, 11 2021.
  * [118] Zhoufeng Liu, Zhaochen Huo, Chunlei Li, Yan Dong, and Bicao Li.  Dlse-net: A robust weakly supervised network for fabric defect detection.  Displays, 68:102008, 07 2021.
  * [119] Liu Rong-qiang, Li Ming-hui, Shi Jia-chen, and Liang Yi-bin.  Fabric defect detection method based on improved u-net.  In Journal of Physics: Conference Series, volume 1948, page 012160. IOP Publishing, 2021.
  * [120] José Manuel Amigo and Carolina Santos.  Preprocessing of hyperspectral and multispectral images.  In Data handling in science and technology, volume 32, pages 37–53. Elsevier, 2019.