[][1]

[1]

[1]

[1]

1]organization=University of Georgia, city=Athens, postcode=30602, state=GA,
country=USA 2]organization=University of Virginia, city=Charlottesville,
postcode=22903, state=VA, country=USA 3]organization=University of Florida,
city=Gainesville, postcode=32611, state=FL, country=USA
4]organization=University of Texas at Arlington, city=Arlington,
postcode=76010, state=TX, country=USA

[cor1]Corresponding author

[fn1]These authors contributed equally to this work.

# AGI for Agriculture

Guoyu Lu<EMAIL_ADDRESS>Sheng Li<EMAIL_ADDRESS>Gengchen Mai
<EMAIL_ADDRESS>Jin Sun<EMAIL_ADDRESS>Dajiang Zhu<EMAIL_ADDRESS>Lilong Chai<EMAIL_ADDRESS>Haijian Sun<EMAIL_ADDRESS>Xianqiao Wang
<EMAIL_ADDRESS>Haixing Dai<EMAIL_ADDRESS>Ninghao Liu<EMAIL_ADDRESS>Rui Xu<EMAIL_ADDRESS>Daniel Petti<EMAIL_ADDRESS>Changying Li
<EMAIL_ADDRESS>Tianming Liu<EMAIL_ADDRESS>[  [  [  [

###### Abstract

Artificial General Intelligence (AGI) is poised to revolutionize a variety of
sectors, including healthcare, finance, transportation, and education. Within
healthcare, AGI is being utilized to analyze clinical medical notes, recognize
patterns in patient data, and aid in patient management. Agriculture is
another critical sector that impacts the lives of individuals worldwide. It
serves as a foundation for providing food, fiber, and fuel, yet faces several
challenges, such as climate change, soil degradation, water scarcity, and food
security. AGI has the potential to tackle these issues by enhancing crop
yields, reducing waste, and promoting sustainable farming practices. It can
also help farmers make informed decisions by leveraging real-time data,
leading to more efficient and effective farm management. This paper delves
into the potential future applications of AGI in agriculture, such as
agriculture image processing, natural language processing (NLP), robotics,
knowledge graphs, and infrastructure, and their impact on precision livestock
and precision crops. By leveraging the power of AGI, these emerging
technologies can provide farmers with actionable insights, allowing for
optimized decision-making and increased productivity. The transformative
potential of AGI in agriculture is vast, and this paper aims to highlight its
potential to revolutionize the industry.

###### keywords:

Artificial General Intelligence (AGI) Agriculture

##  1 Introduction

Since the artificial intelligence application AlphaGo defeated the world Go
masters, artificial intelligence has become a hot topic again, particularly
after the release of ChatGPT and GPT-4 [125]. Artificial general intelligence
(AGI) is that machines can think like humans and perform a multitude of
general tasks by means of transfer learning and various other modalities
[198]. Over the past few years, Large Language Models (LLMs) [67] such as
GPT-3.5 used in ChatGPT have attracted a lot of attention and interest. More
recently, GPT-4 is a multimodal language and vision system with the ability to
recognize and describe pictures. Once launched, it impresses everyone with its
ability to match human intelligence. With further development, AGI is expected
to have deeper and broader impacts over agriculture.

Despite agriculture being recognized as a fundamental pillar for national and
global economic development, the scientific literature on the implementation
of Artificial Intelligence (AI), particularly Artificial General Intelligence
(AGI), in agriculture remains relatively scant. This underrepresentation in
the literature is striking given the importance of agriculture as a crucial
component of global food security and the potential for AI to enhance
agricultural productivity and sustainability. With the recent fast development
of AGI, we expect it will play an increasingly critical role in the future
agriculture applications. Therefore, in this article, we will discuss the
applications of AGI in agriculture.

The application of computer vision mainly includes target detection [138],
image segmentation [82, 63], localization [99, 100], and image classification
[64]. AGI technology can apply computer vision technology to deeper and wider
fields. In the field of agriculture, AGI technology can also enable and
promote the development of a wide range of applications, such as crop pest
identification, disease detection, etc. In addition, AGI can also be used in
precision farming, which involves the use of advanced sensors and algorithms
to analyze data and optimize crop yields. This AGI technology can help farmers
make informed decisions about irrigation, fertilization, and other farming
practices, leading to increased productivity and efficiency. AGI can also be
used in developing new crop varieties through computational breeding, which
could speed up the process of developing more resilient, high-yield crops.
Another potential application of AGI in agriculture is in farm automation,
where robots and drones equipped with advanced computer vision and machine
learning capabilities could perform a wide range of tasks, from planting and
harvesting to monitoring crops and livestock. In comparison with major
computer vision or other specific AI (i.e., artificial narrow intelligence
(ANI) [84]) based methods, AGI could potentially provide more comprehensive,
smarter, and richer information with fewer middle steps, as illustrated in
Fig. 1.

Notably, AGI could support agriculture development from multiple modalities,
such as images, sound, robotics, knowledge graph, NLP, etc., and potentially
fuse them to support decision-making. One significant difference between AGI
and AI in agriculture is their ability to generalize and learn from new
situations. AGI systems have the ability to transfer learning from one domain
to another and adapt to new situations without human intervention. This means
that AGI can learn from new data and experiences in a way that AI cannot. On
the one hand, AGI could enable extensive new applications; on the other hand,
AGI can significantly enhance the performance of existing AI systems on the
same tasks.

AGI has the potential to revolutionize the way farmers interact with
agriculture robots. Using natural language processing and machine learning,
AGI can enable robots to understand and respond to human commands, whether
they are given through voice or text. This will help simplify the operation of
agriculture robots, reduce the learning time for new users, and make them more
accessible to farmers. One potential application of AGI in agriculture is the
development of smart farming assistants that can perform tasks such as
planting, watering, and harvesting crops in response to natural language
commands. These assistants can use machine learning algorithms to optimize
their recommendations based on real-time data from the farm and adapt to
individual farmer’s preference.

AGI can also facilitate the coordination and cooperation of robots to optimize
farm operations. For example, robots equipped with AGI can communicate with
each other to prioritize tasks, optimize resource utilization, and avoid
collisions and other accidents. By enabling robots to work together more
efficiently, AGI can help farmers increase their productivity, reduce costs,
and make better use of their resources.

For breeding and phenotyping, by analyzing large volumes of phenomic and
genomic data and other environmental factors, AGI can help breeders to
identify and select the most promising plant or animal traits with greater
precision and efficiency than traditional methods. Based on breeders’ needs,
AGI can possibly generate predictive models that can forecast the performance
of different breeding combinations based on phenomic and genomic data and
other relevant factors. These models can use machine learning algorithms to
optimize breeding strategies and predict the outcome of different breeding
combinations.

More apparent, broader, and quicker impacts that AGI can bring to agriculture
are its capability to enhance the effectiveness of existing AI systems. An
example is the identification of types of crop pests and diseases. Crop
diseases and insect pests are one of the main disasters in agriculture. They
have the characteristics of various types, great impact, and frequent
outbreaks. At present, in order to control the occurrence of pests and
diseases, most farmers spray pesticides blindly, which inevitably causes a
series of problems such as environmental pollution and food safety [126, 164].
In addition, the identification of crop diseases and insect pests mainly
depends on the experience of farmers and experts. When dealing with large-
scale monitoring of crop diseases and insect pests, current AI systems need to
rely on data that have already been well-labeled to train comprehensively. It
is easy to miss the rescue measures and the best time for diseased crops. AGI
technology can lower the threshold for identifying types of crop diseases and
insect pests. Farmers can take photos of plant diseases by their mobile
phones, and then upload them to the AGI model. AGI will be able to understand
the input instructions and tell farmers how to manage identified pests and
diseases, e.g., which pesticide to buy. In addition, farmers can also capture
the sound of pests in the field by recording equipments and upload them to the
AGI model. AGI can then recognize the sound characteristics of pests and
perform speech recognition to identify crop pests. In contrast with normal AI
and CV algorithms, most notably, AGI can help farmers identify previously
unknown pests and diseases by learning from new data and recognizing patterns
that may not be apparent to humans. This ability to generalize and learn from
new situations could significantly enhance the effectiveness of agriculture
solutions and improve the overall resilience of the industry.

Figure 1: Main pipeline of AGI for agriculture applications in comparison with
specific AI-CV-based agriculture image processing framework.

In another scenario, weeds are similar in color to crops, and their growth
also requires sunlight and water resources. Weeds will invade the living space
of crops and rob the crops of nutrients. If the density of weeds in the field
is too high, it will seriously affect the yield and quality of crops.
Therefore, weeding is an inevitable and important part of agricultural
production. AGI technology can now help people get rid of weeds. AGI has the
potential to outperform traditional AI systems. AGI can be trained to identify
and differentiate between plants and weeds, and then selectively remove weeds
without harming the crops. This is possible through the use of advanced
computer vision and machine learning algorithms that allow AGI to recognize
and respond to new situations and data. On the one hand, through the general-
purpose vision and reasoning capabilities in AGI technology, people can
accurately locate weeds, guide people to quickly reach the weed growth area,
and inform people of suitable pesticides, so that people can have different
responses to different weeds, and make targeted weed control. Traditional AI
systems for weeding typically rely on pre-programmed rules and limited data
sets, which may not be able to adapt to new situations or recognize emerging
weed species. AGI systems can learn from their experiences and adapt to new
data, allowing them to become more accurate and efficient over time. Another
way is to connect AGI to intelligent robots, so that the robots can quickly
and accurately identify weeds and spray pesticides precisely. This will reduce
the use of pesticides such as herbicides, reduce environmental pollution
caused by pesticides, and avoid labor shortages, further reducing costs.

The quality of crop seeds not only determines the survival rate of seedlings,
but also has an important impact on the harvest and quality of crops. High-
vigorous seeds have strong resistance to stress. When planted in the field,
they have a high germination rate, rapid and neat emergence, and can produce
high-yield and high-quality crops; low-vigority seeds have poor stress
resistance, and are prone to rotten seeds and dead seedlings, resulting in
crop yield reduction. AGI can screen unknown seeds by extracting excellent
seed traits and their corresponding data, and retain seeds with excellent
shapes. AGI can further support seed selection based on multiple clues, such
as their size, plumpness, external geometric characteristics, color
characteristics, and whether there is mildew, etc.

Fruit grading is to strictly screen the fruits before entering the market,
remove rotten fruits, and classify them according to the quality of the
fruits. Through the detection of fruit photos, AGI can package, transport and
sell different grades of fruits separately, reduce storage losses, and discuss
prices according to quality when selling to obtain maximum economic benefits.
AGI has a significant advantage over traditional AI in its ability to
generalize and learn from new situations. Therefore, AGI can be trained to
recognize various fruit quality factors such as ripeness, size, color, and
shape, and accurately sort fruits into different quality categories such as
"high quality," "average," and "not suitable to sell." In contrast, when fruit
shape, size, color, and especially rotten regions and degrees differ from
limited training data, the screening effect may be reduced.

Furthermore, AGI has the potential to perform a wide range of tasks in
agriculture autonomously. With its ability to understand and interpret natural
language and visual cues, AGI can make decisions and take actions
independently without human intervention. This level of autonomy is not
possible with current AI systems and could revolutionize the way farmers
manage their crops and livestock.

With the examples mentioned above, AGI could provide critical support and
service for agriculture development based on information extracted from
multiple modalities and knowledge domains. The following sections of this
paper will articulate AGI for agricultural applications from multiple
perspectives in detail, including AGI background, agriculture image
understanding, NLP, knowledge graph, infrastructure, robotics, and their
applications to precision crops and precision livestock, respectively.

##  2 Background

In this section, we briefly introduce how AI and AGI techniques, such as
natural language processing (NLP), can be adopted to advance a wide variety of
agriculture applications. Texts are probably the most accessible data sources
in many domains including agriculture, considering the vast amount of
documents (e.g., scientific papers, technical reports, surveys, news, and
social media data). Although some pioneering efforts use NLP techniques for
agriculture applications, customizing NLP models for specific agriculture
applications has been largely underexplored. In the following, we discuss
several types of NLP techniques, including both traditional NLP techniques and
the most recent large language models (LLMs), which have been used (or could
be potentially used) to assist agriculture applications.

Information Extraction. As a fundamental task in NLP, information extraction
focuses on extracting structured information from unstructured or semi-
structured documents. It is also an essential step for many other NLP tasks
such as knowledge graph construction [141]. Some recent efforts in the
agriculture domain can be found in this survey paper [123]. Most work in this
area focuses on applying classical NLP techniques (e.g., rule-based methods
and learning-based methods) for subtasks like named entity recognition (NER)
and then creating ontology or knowledge graph that is specific to agriculture.
Recently, transformer-based language models like BERT have also been used for
entity and relation extraction [133].

Semantic Matching. Considering the diversity in language representations,
semantic matching is a key technique that enables the correct matching of
semantically similar entities with varying lengths (e.g., words, phrases,
sentences, documents). One recent application of semantic matching in the
agriculture domain is to establish the mapping between food descriptions and
nutrition database [140]. In particular, a food-related text corpora (e.g.,
food and agricultural literature) is collected for pretraining transformer-
based language models. And semantic matching between food and nutrition is
then performed in the embedding space, by formulating an answer selection
problem. Moreover, semantic matching could play an essential role in fusing
agricultural text data from different sources.

Question Answering and Dialogue System. Question answering (QA) and dialogue
systems are fundamental problems in NLP [34, 86]. The models are expected to
accurately understand the question and context, and then generate informative
responses. A direct extension of the QA technique is dialogue systems such as
chatbots. By far, some chatbots have been developed in the agriculture domain,
such as answering questions from farmers [122, 158, 58]. For instance, a
recurrent neural network (RNN) based chatbot is designed to deal with
questions about soil testing, plant protection, and nutrient management [158].
These QA systems or chatbots, however, have very limited capability due to the
small model size and insufficient training data.

Pretrained Language Models (PLMs). Language modeling has become the mainstream
technique in NLP due to its effectiveness and remarkable generalizability. In
particular, transformer-based pretrained language models can serve as base
models for lots of NLP applications. PLMs such as BERT are usually pretrained
by tasks like masked language modeling (MLM) and next sentence prediction
(NSP). Since these PLMs are pretrained in general domains using data from
Wikipedia and BookCorpus, a fine-tuning step is usually required for specific
domains. By fine-tuning the base PLMs with high-quality text corpus from a
specific domain, customized language models could be developed. Some
representative examples include BioBERT [88], PubMedBERT [59], and LEGAL-BERT
[33]. In [140], a large-scale corpus of agricultural literature with more than
300 million tokens is used to fine-tune the generic BERT into AgriBERT. Such
domain-specific language models could be potentially used to assist many
agricultural applications, such as agricultural marketing, agricultural social
media analytics, etc.

Large Language Models (LLMs). LLMs usually refer to PLMs with more than
billions of model parameters, such as the GPT (Generative Pre-trained
Transformer) models (GPT-1 [135], GPT-2 [136], GPT-3 [26], InstructGPT [127],
ChatGPT, and GPT-4 [125]), LLaMA (Large Language Model Meta AI) [163], and
BLOOM [147]. For instance, GPT-3 and BLOOM have 175 billion parameters, and
LLaMA has 65 billion parameters. Similar to traditional PLMs like BERT, these
recent LLMs also follow the Transformer architecture [167], but they differ
from each other in training objectives, data sources, optimization details,
etc. For example, BERT only adopts the encoder of Transformer as it focuses on
learning language representations, while GPT models adopt the decoder of
Transformer, as their major objective is to generate realistic text. Owing to
their huge model sizes and effective incorporation of human feedback (e.g.,
prompts), the latest LLMs like ChatGPT and GPT-4 have not only achieved
amazing performance in traditional NLP tasks like language understanding,
language generation, and question answering, but also demonstrated remarkable
capabilities in zero-shot learning and reasoning [96]. For instance, ChatGPT
has shown a very promising capability in inductive reasoning and deductive
reasoning [67]. GPT-4 is a large multimodal model that can deal with both
language and visual inputs. In addition, LLMs are also effective tools for
data augmentation [40], which is very helpful for certain real-world scenarios
with limited data. These exciting observations showcase that the LLMs-based
techniques may enable a promising roadmap towards AGI. For agricultural
applications, the LLMs would be able to significantly advance the processing,
understanding, and interpretation of agricultural literature and documents.
Moreover, the zero-shot learning and reasoning capabilities of LLMs would also
greatly facilitate various decision-making problems in the agriculture domain.

In the following sections, we will discuss how the AGI techniques (such as
LLMs and large multimodal models) could be leveraged to advance a broad range
of research areas and applications in agriculture. Sections 3-7 focus on
several research topics in agriculture, involving different types of data
(e.g., images, knowledge graphs) and various applications (e.g., precision
livestock and crops, agricultural robotics). In Section 3, we will introduce
AGI for agriculture image understanding, such as managing metadata in breeding
studies, generating images for data augmentation, multi-task scheduling and
compositing, few-shot learning, and domain adaptation. Section 4 discusses
several potential aspects where knowledge graph techniques and AGI are jointly
exploited for agricultural applications. Section 5 describes several possible
ways of using AGI for agriculture robotics. Section 6 presents a few case
studies, including AGI for precision farming and phenomics, introduces, AGI
for precision livestock, and AGI for agricultural infrastructure. The last
section concludes this paper.

##  3 AGI for Agriculture Image Understanding

###  3.1 Generative AI for Agricultural Data

Recently, generative AI models such as the Stable Diffusion [142] model have
shown impressive high-quality generation of photorealistic images and videos
[65]. Similar breakthroughs have occurred in other domains such as speech
[134], all powered by large-scale generative AI models. Multi-modal LLMs are
inherently generative models [57]. They can be fine-tuned to capture the
complex distribution of rich agriculture data in satellite images, videos,
hyperspectral sensing data, 3D point clouds, as well as time-series measures.
Such generative models can empower the following tasks:

Training data generation. One limitation of applying specialized computer
vision algorithms such as PointNet [132] or MaskRCNN [63] to agricultural
visual data is the scarcity of training data and labels. Obtaining high-
quality data are time-consuming and labeling them properly is even more
expensive [201]. One possible solution is to build a digital environment that
generates synthetic data111https://www.nvidia.com/en-us/omniverse/ but there
is usually a domain gap between the simulated environment and the real world.
Complex solutions such as domain adaptation [39] have to be applied to address
this issue. On the other hand, multi-modal generative LLMs, once fine-tuned to
the target agricultural data domain, can generate a large amount of training
data and labels to construct an augmented training set that closely resembles
the original data distribution [40]. Moreover, text-based generation models
can synthesize images [142] and videos [65] that fit specific text
descriptions that describe a specific scene. They can be used to generate
customized agricultural visual data to fine-tune advanced computer vision
algorithms.

Multi-modal data editing and manipulation. Agricultural data show a wide range
of variation due to changes in season and weather. It is extremely difficult
to collect data that capture all the variations. Meanwhile, generative AI
models show extraordinary abilities to edit and manipulate data with various
attributes. They can be used to create variations of the original data for
certain characteristics. Previous work on face editing show promising results
such as changing a person’s hair color and facial expression [151]. In terms
of image weather domain translation, generative adversarial networks (GAN)
have demonstrated great potential in transferring [53, 203] ground-level
images into photorealistic synthetic images under different weather conditions
[90, 70] and different extreme climate events [148]. Recently, diffusion-based
image synthesis models such as SDEdit [117] also show their powerful image
editing capabilities without the requirement of task-specific training.
Corresponding to LLMs, large vision models are also developed, such as SAM for
image segmentation [82]. In the context of agricultural imagery, for example,
generative AI models can change the time from day to night [131] and the
weather from sunny to rainy. These variations can help in training more robust
models for critical agriculture tasks.

###  3.2 LLMs for Multi-Task Scheduling and Compositing

The visual understanding of agriculture data usually consists of multiple sub-
tasks. For example, for the main task of counting the number of healthy
chickens on a poultry farm using cameras, sub-tasks such as object detection
[138], instance segmentation [63], object tracking [175], and image
classification [64] are needed. It requires domain knowledge in agriculture as
well as in computer vision and machine learning to design and conduct such a
workflow. It is simply not possible to scale. Taking another example, plant
root phenotyping [104, 103, 102] would involve 3D root structure modeling,
root branch counting, length and angle estimation, biomass measurement,
distribution pattern, etc, each of which involves extensive efforts.

With the semantic understanding and reasoning ability of LLMs and their
multimodal variants, it is now possible to make cutting-edge specialized
vision and language models more accessible to important agriculture
infrastructures such as farms. A recent study shows that ChatGPT is able to
understand a request in natural language, extract useful visual and textual
information, choose relevant vision and language tasks, and interpret the
results back to humans [152]. LLMs can composite efficient and effective
workflows from multiple vision and language tasks to perform critical
agriculture tasks, all from simple and intuitive natural language instruction
and without human intervention along the way. With the development of LLMs, it
is expected that with the language input, the phenotype information could be
directly provided to the agriculture researchers and breeders, as in Fig. 2.

Figure 2: Plant root phenotyping based on AI-CV (computer vision)
methodologies versus AGI. AI-CV requires extensive specific CV knowledge to
process each task, while AGI has the potential to directly provide needed
phenotype information with the human prompt.

###  3.3 Few-Shot Learning

Unlike human intelligence which can infer new knowledge based on limited or
zero experiences, most machine learning approaches, such as the traditional
supervised learning techniques, often require thousands of labeled examples in
order to learn simple patterns. To bridge this gap between human intelligence
and AGI, few-shot learning (FSL) [48, 50] has been proposed. In particular,
FSL approaches focus on “meta-learning”, that is, learning a model that can
generalize to novel tasks specified at inference-time [169]. In other words,
FSL aims to emulate the remarkable generalization ability of human
intelligence, by quickly adapting to new tasks that contain insufficient
samples. This is very helpful for scenarios where data collection and labeling
is difficult and expensive, such as agricultural applications.

Depending on the sample availability, FSL can be divided into three
categories: Few-Shot (two to five samples per class), One-Shot (a single
sample per class), and Zero-Shot (classifying unseen classes without any
training samples). FSL has shown promising results in various domains and
applications, including computer vision [17, 155, 83, 145] and natural
language processing [187, 14]. Based on how to leverage the prior knowledge,
existing FSL works can be categorized into three folds: 1) Data Augmentation.
Some methods try to directly augment the training dataset by generating new
samples from existing ones, transforming samples from a weakly labeled or
unlabeled dataset, or transforming samples from a similar dataset. These
augmentation processes are usually guided by hand-crafted rules, such as
translation [17], flipping [155], reflection [83], and rotation [145]
operation for image data, or learning a set of transformations or models to
conduct data augmentation [118, 150, 85]. 2) Hypothesis constraint. To
approximate the ground-truth distribution, models may need to determine a
hypothesis space that minimizes the distance between the optimal prediction
and the ground-truth distribution. As a larger hypothesis space requires more
data samples to explore, some FSL methods try to constrain the searching space
of the hypothesis via jointly learning multiple related tasks with shared
model parameters [120, 195, 196] or restricting the form of distribution from
prior knowledge [79, 87, 144]. 3) Prior knowledge. When the training dataset
is not large enough, FSL methods may leverage the prior knowledge to intervene
in the searching strategy of the parameters, either by providing a good
initialization of the parameters [13, 29, 66] or directly learning an
optimizer to output search steps, such as changing the search direction or
step size [8, 137].

Although few-shot learning approaches have received a significant amount of
attention over the past decade [77, 162, 80, 182, 188, 46], they have rarely
rivaled the popularity of their fully-supervised brethren. Additionally, most
such approaches have limited generalizability, applying only to a single type
of task, such as image classification [169], object detection [80, 182], or
multi-object tracking [188]. An approach designed for, say, object
classification cannot trivially be transferred to object detection, even
though the two problems are related. One of the more surprising attributes of
LLMs is that they exhibit a natural aptitude for few-shot learning [25]. Given
the rapid progress of LLMs towards ever-more-general problem solving, it is
not unreasonable to assume that we are entering a future where the current,
data-hungry supervised learning paradigm is obsolete. Currently, a significant
challenge for agricultural deep learning is a lack of annotated data, leading
to a variety of attempts to reduce the need for it [2, 5, 16, 23, 54, 55, 114,
130, 165]. Suitable data for a particular crop may not exist at all, may not
be annotated properly, or may not be publicly available.

Advanced, multi-modal LLMs have the potential to solve these problems once and
for all, but significant questions remain. Next-generation LLMs are only just
beginning to integrate visual inputs [125]. For its part, OpenAI is fairly
tight-lipped about what exactly GPT-4 is capable of in this domain, and we
likely won’t know for sure until it is tested rigorously and independently.
One clear risk is that LLMs trained on internet-scale data will fail to
generalize well to the agricultural vision domain because agricultural images
tend to be very different from the majority of pictures on the internet. In
this situation, we would be forced to rely on the model’s innate few-shot
learning capabilities, which may or may not be up to the task. Additionally,
there are many tasks of interest to the agricultural robotics community that
require taking more obscure modalities (eg. point clouds or hyperspectral
cubes) as inputs, and/or have outputs that cannot be expressed as text. Models
such as GPT-4 would likely need to be extended at the architectural level in
order to support such use cases. Given the fact that state-of-the-art LLMs are
mostly proprietary, shrouded in secrecy, and impossible to train without
millions of dollars invested in compute capacity, the research community might
be waiting quite a while for these features.

If we take the optimistic view that near-future LLMs will be capable of at
least some agricultural vision tasks, what would that enable? Already, thanks
to the proliferation of tools like YOLOv5 [78], simple computer vision tasks
such as object detection can be accomplished by non-experts with little
specialized training. Clearly, a reliable few-shot approach to agricultural
computer vision would only make things easier by freeing us from the tyranny
of data annotation, one of the last major pain points of computer vision
workflows. Projects that would previously be difficult or slow due to the need
to collect and annotate data could be deployed significantly faster. In
addition, because of their language processing capabilities, vision-capable
LLMs would be ideal for managing image-associated metadata, perhaps gaining
the ability to derive textual metadata automatically based on an image input.
Overall, LLMs stand to substantially accelerate the cycle of innovation within
agricultural computer vision, and will (hopefully) usher these technologies
into widespread use amongst farmers.

###  3.4 Domain Adaption

Though AGI has made breakthroughs in various application fields for its
impressive performance in prediction and generalization, it unavoidably
requires large amounts of labeled data which may not be realistic in real-
world scenarios due to the considerable time and expensive labor forces.
Unsupervised domain adaptation (UDA) provides a promising way to address this
issue: it aims to adapt the models well-trained on one domain (source domain)
to another domain (target domain) with different distributional
characteristics, without the need for labeled data in the target domain [172,
47]. The key step of UDA is to find the discriminant and domain-invariant
features that can link the labeled source domain and the unlabeled target
domain in a latent space. In the past decades, many UDA methods have been
developed and evaluated [51, 93, 98, 154, 194, 204, 207]. In addition to UDA,
some other practical domain adaptation problems have also been extensively
studied in the literature, such as universal domain adaptation [143, 205],
class-imbalanced domain adaptation [153], few-shot domain adaptation [120],
open-set domain adaptation [28], and domain generalization [200, 206].

In recent years, domain adaptation has been applied to some agricultural
applications, such as plant classification [56], leaf counting [166], corn
yield prediction [107], agricultural land extraction [191], and land cover
segmentation [18]. For instance, an unsupervised domain adaptation approach is
proposed to transfer plant classification systems to new field environments,
crops, and robots [56]. An adversarial domain adaptation method is proposed
for agricultural land extraction using remote sensing images [191]. In
general, most previous work primarily relies on convolutional neural networks
(CNNs) to learn domain-invariant representations [173, 180] among different
domains. With the surge of Vision Transformers (ViT) for vision tasks, a few
ViT-based studies have been developed and demonstrated significantly superior
transferability over their CNN-based counterparts [183, 160, 181]. Moreover,
the latest development on large multimodal models such as GPT-4 would lead to
new formulation and modeling of domain adaptation, especially for certain
application domains like agriculture. In traditional domain adaptation
settings, the source domain usually offers auxiliary knowledge that is limited
to a narrow and specific scenario. However, large multimodal models have
already encoded rich commonsense knowledge, and they also have remarkable
reasoning capabilities. Thus, the problem of transferring knowledge from large
multimodal models to target domains should be carefully reformulated.
Meanwhile, new methodologies on transfer learning, cross-domain representation
learning, and visual/textual prompt-based knowledge transfer should be further
developed.

##  4 AGI for Agricultural Knowledge Graphs

Represented as labeled directed multigraphs, knowledge graphs (KGs) are a
novel paradigm for the representation, retrieval, and integration of data from
various data sources across domains [22, 75]. KGs have emerged as a promising
solution for various academic and industry challenges [124]. In the following,
we discuss several potential aspects where knowledge graphs technologies and
AGI can work together and achieve a more intelligent agriculture system.

Accurate and Interpretable AGI for Agriculture Tasks.  Although the recent
foundation models show promising performances on various language, vision, and
robotic tasks [24], they are also criticized for generating inaccurate and
misleading results [163, 68]. Knowledge graphs have been shown to be
particularly useful to guide the predictions of language models [186] on
different tasks and help ground the generated answers on a particular subgraph
[159] to ensure the accuracy of generated answers and improve the model
interpretations. This capability is particularly useful for many real-world
tasks in which accurate and interpretable predictions are critical such as
tasks in medical and agriculture domains. With the contextual information
provided by knowledge graphs, AGI can be better used for agriculture tasks
such as crop monitoring, crop yield forecasting, fertilizing, and so on.

Text-Knowledge Fusion for Agriculture Tasks.  With the recent development of
large language models (LLMs) which are generally treated as the major driving
force behind AGI, knowledge graphs are commonly treated as an additional data
source that can complement text data for language model pretraining or
finetuning to significantly boost their performance on various downstream
tasks, especially difficult question answering tasks that require complex
reasoning [159, 111, 177, 3, 110, 193, 186]. In the agriculture domain,
AgriBERT [140] has shown that augmenting text data (i.e., question answering
pairs) with additional knowledge from an agriculture knowledge graph (i.e.,
FoodOn knowledge graph) can significantly boost model performance on various
NLP tasks in the food and agriculture domain.

AGI for Agriculture Knowledge Graph Construction.  The recent developments of
foundation models foster the usage of AGI on knowledge graph construction.
Instead of performing knowledge triplet extraction in a supervised manner [38]
or by using seed samples [37], Mai et al. [108] demonstrate that by using
simple prompts, GPT-3 can accurately recognize place names from unstructured
text and outperforms various state-of-the-art fully supervised toponym
recognition models. This shows the potentials of AGI for entity recognition
and entity linking which are two critical tasks related to knowledge graphs.
Recently, GraphGPT222https://github.com/varunshenoy/GraphGPT has been
developed as a GPT-3 based knowledge graph generation tool that can update a
given knowledge graph or create a new knowledge graph given natural language
inputs. Most widly used knowledge graphs, either general-purposed KGs such as
DBpedia [10], and Wikidata [170], or domain-specific KGs such as FoodOn [45],
GeoNames333https://www.geonames.org/ [4] LinkedGeoData [11], GNIS-LD [139],
and KnowWhereGraph444https://knowwheregraph.org/ [74, 75], are constructed
manually, or semi-automatically. AGI-based automatic knowledge graph
construction is a promising research direction, especially for domains where
there are fewer knowledge engineers available such as the agriculture domain.
The automatically constructed agriculture knowledge graph can provide
additional contextual information and facilitate various agriculture decision-
making processes.

Complex Knowledge Reasoning for Agriculture Tasks.  Many agriculture tasks
require complex reasoning [20]. For example, given a UAV image of a cropland
of soybean, an agricultural scientist or a farmer expect an AGI can 1) first
detect whether there are some abnormal symptoms given the appearance of
soybean leaves (e.g., water-soaked spots on leaves); then 2) determine the
name of this problem (e.g., bacterial blight); next, 3) determine the causes
of this disease (e.g., pseudomonas syringae); and finally, 4) determine how to
treat this disease (e.g., spray bactericides). Currently, one popular way to
enable a large language model (LLM) to do complex reasoning is the usage of
chain-of-thought prompting [174] where a few chains of thought demonstrations
are provided as exemplars in prompting to explicitly force the LLM to conduct
complex reasoning. The idea is to allow LLM to decompose a multi-step
reasoning problem into intermediate steps that are solved individually,
instead of solving an entire multi-hop reasoning problem in a single forward
pass. However, such an idea is to reason in an implicit manner which is prone
to errors and hard to debug. By explicitly encoding the relationships among
crop symptoms, diseases, bacteria, and treatments into an agriculture
knowledge graph, we can ask the LLM to follow the path in this graph and
conduct explicit multi-hop reasoning just as the way Neural Symbolic Machine
adopts [91].

Applications of Knowledge Graphs to Agricultural AGI. Knowledge graphs
describe the properties and relationships of real-world entities, providing
benefits to a variety of agricultural applications [36], such as enhancing
search engines, Q&A systems, recommendation systems and content generation
with greater intelligence.

  * •

Search Engines. Agriculture search engines could better connect farmers and
professionals to the information they need [71]. Traditional search engines
work by crawling and indexing web content, accepting queries, and ranking
results according to relevance [149]. Due to the emerging general
intelligence, the recent large language models (LLMs) such as ChatGPT and
GPT-4 [125] could be used to generate results for searches, potentially
disrupting the search engine industry. The concept of knowledge graphs gained
great popularity since its launch by Google’s search engine, associated with
the Knowledge Vault framework [44] to build large-scale knowledge graphs.
Knowledge graphs can complement LLMs by providing them with structured and
reliable information.

  * •

Q&A Systems. Question Answering (Q&A) aims to provide accurate and concise
natural language answers in response to questions posed by users [202, 73,
110]. The recent chatbot-style language models, if trained with large amounts
of agricultural data from various sources (e.g., weather patterns, and pest
control), have the potential to answer questions related to various
agricultural domains. Nevertheless, LLMs may generate content that lacks
factual accuracy or omits important knowledge, where KGs could be applied to
mitigate the problem. One strategy is to enhance language representation
learning with knowledge entities [197, 177]. Another way is to directly learn
knowledge graph embeddings and then integrate the learned entity and relation
embeddings into the QA pipeline [69, 109, 146].

  * •

Recommender Systems. Agricultural recommender systems can facilitate the
dissemination of authentic information and help farmers make more informed
decisions on how to optimize their production, e.g., suggesting which
agricultural products to use, such as seeds, fertilizers, and pesticides [52].
The recent conversational AI [161] could even interact with users to better
help them find products/services. By utilizing the side information in KGs, it
is possible to capture user preferences more accurately [60]. Typical
strategies of combining KGs include learning informative knowledge graph
embeddings [171, 94] and conducting path-based reasoning over KGs [176, 106].

  * •

Content Generation. The advanced language processing capabilities of LLMs can
simplify and speed up repetitive mental tasks of agricultural professionals,
such as recording, report generation, and training materials
generation555https://agtecher.com/how-openai-and-chatgpt-can-be-used-in-
agriculture/#help-agri. The incorporation of KGs could make the content
generation process more controllable [189]. Besides knowledge graph embedding
[199, 30] and path-based reasoning [97], it is also possible to apply graph
neural networks to encode high-order information towards text generation [15].

##  5 AGI for Agricultural Robotics

By enhancing the decision-making process of robots, AGI can improve the
efficiency of executing certain tasks, particularly in the agricultural sector
where some tasks still rely heavily on manual labor [179]. The application of
AGI in agricultural robotics can enable robots to perform a range of tasks
that currently require many single-task AI models to perform [178, 72]. It
also has the potential to revolutionize the design of agricultural robots by
leveraging high-level function libraries and prompt engineering [168]. With
AGI, agricultural robots can optimize various tasks and minimize waste, making
them a promising technology for precision agriculture.

AGI’s ability to understand natural languages and reasoning can greatly
improve the interaction between humans and robots, thereby lowering the
technical barriers for farmers to use agricultural robots. Additionally, AGI’s
ability to comprehend images can enable robots to better understand their
surroundings through computer vision, which includes identifying potential
safety hazards. The future scenarios of interactions between humans and
agricultural robots are illustrated in Fig 3. These are just a few examples of
how AGI can enhance the applications of agricultural robots.

  * •

Human-robot interaction. AGI can significantly simplify the interaction
between humans and robots by allowing users to control robots using natural
language, rather than typing out commands. This reduces barriers to usage for
those without a technological background and saves costs on training certified
operators.

  * •

Robot safety. AGI’s ability to detect safety hazards in real-time can
significantly improve the safety of agricultural robots. For instance, if a
robot detects a safety hazard, it can immediately stop its operation or alert
the farmer, preventing potential accidents or injuries. The robots can also
monitor the progress of agricultural tasks and identify potential damage to
plants when command execution fails. Additionally, AGI can help robots learn
from their experiences and improve their performance over time, reducing the
likelihood of future safety hazards.

  * •

Human-robot collaboration. AGI can enable robots to coordinate more
effectively with humans in more complex tasks, such as harvesting. For
example, a harvest aid robot can offer more efficient and responsive
assistance to a human worker when it can understand the worker’s intentions by
recognizing their gestures and language, improving the overall efficiency of
harvesting. Additionally, the robot can identify potential hazards and avoid
harming human workers.

Figure 3: Future scenarios of enhanced human interaction with agricultural
robots with the aid of AGI to perform farming operations more efficiently.

AGI has several advantages, including the ability to integrate historical and
real-time data from various sources such as weather, environment, soil, and
plant data. This can help robots generate optimal strategies for specific
agricultural tasks in real-time. The applications of AGI in agriculture are
numerous, including enhancing the efficiency of the following tasks:

  * •

Planting. AGI can be used to help robots plant crops more efficiently by
providing real-time data on soil quality, moisture levels, and other factors
that affect seed growth. By using this data, robots can plant seeds at optimal
depths and spacing, improving crop yields and reducing waste.

  * •

Weeding. AGI can help robots differentiate between weeds and crops and
eliminate weeds using targeted spraying, laser, or mechanical removal without
damaging the crops, which can greatly reduce the usage of herbicides and their
impacts on the environment.

  * •

Fertilizing. AGI can be used to detect the plant growth status and identify
plants suffering from malnutrition. Combined with data on soil quality, robots
can adjust the amount and timing of fertilizers to optimize crop yields and
reduce waste.

  * •

Harvesting. AGI can help robots to detect the ripeness of the crops and
determine the optimal harvesting timing, enabling robots to selectively
harvest the ripe crops to ensure good quality. AGI is particularly useful to
enable robots to perform fruit picking for certain crops that currently can
only be harvested manually. Using AGI, robots can learn to recognize the
specific features of each crop and adjust their picking strategy accordingly
to improve harvesting efficiency.

  * •

Irrigation. AGI can utilize weather data from online sources, local
environmental data and soil moisture from IoT devices, and plant growth from
crop monitoring to optimize irrigation schedules. AGI can help robots
precisely control the irrigation amount for each plant, reducing wasting water
and improving crop yields.

  * •

Crop scouting. AGI can help robots to monitor the growth of the crop and
identify potential issues in the early stage. By analyzing the crop data
collected using sensors and cameras, AGI can monitor the physiological
activities of individual plants, which are important information to make
decisions for other agricultural tasks. AGI can also help robots detect
problems such as water stress, nutrient deficiencies, pest infestations, and
disease outbreaks, enabling farmers to take corrective action before crop
yields are affected.

  * •

Phenotyping. AGI can be a great tool for plant breeders to quickly extract
phenotypical traits from the large amount of data collected by robots. AGI can
be used to automatically organize data, perform basic statistical analysis,
visualize data, and generate reports, saving breeders time on data preparation
and analytics.

##  6 A Few Case Studies

###  6.1 AGI for Precision Farming and Phenomics

The data generated from precision agriculture is typically in the form of raw
data collected from various sensors, such as images, point clouds, spectra,
and time-series measurements. In contrast, Large Language Models (LLMs)
traditionally operate in the text domain. Therefore, the application of LLMs
directly to Agriculture 4.0 may appear to be the naive coupling of two
buzzwords. This may lead to initial skepticism regarding the potential
usefulness of LLMs in the agriculture domain.

Figure 4: The current version of ChatGPT is extremely capable at converting
unstructured text data to structured data.

In practice, however, many advances in agriculture are driven by experiments
that produce large amounts of metadata, often expressed in natural language.
For example, Papoutsoglou et al. [128] attempts to formalize the structure of
plant phenomics data, but the sheer complexity of their approach increases the
barriers for potential users, as well as the risk of mistakes. LLMs have the
potential to partially or fully automate tasks such as structuring
unstructured metadata (Fig. 4), converting metadata from one format to
another, and flagging possible mistakes during data collection. When coupled
with voice recognition technology such as the Whisper APIs, LLMs could even
assist plant breeders working in the field in collecting measurements without
performing tedious and error-prone data entry. Additionally, Bubeck et al.
[27] suggests that next-generation LLMs will be extremely capable tools for
data visualization, potentially making it much easier to glean useful insights
from these text data. LLMs also have the ability to make searching large
quantities of text data much more effective. Overall, LLMs have the potential
to greatly assist researchers in gleaning useful insights from large
quantities of phenotypic data.

###  6.2 AGI for Precision Livestock

Figure 5: Generative AI system for automatic assessment of poultry body weight
and welfare (gait score is walking ability indicator (0, healthy legs, 1,
medium lameness, and 2- severe lameness similar to dead birds).

Poultry and egg production provide valuable and affordable protein sources for
increasing world’s population. In the past decades, the production efficiency
of broiler chickens has been doubled in the US due to continuous innovations
in animal breeding, nutrition management, environmental control, and diseases
prevention [32] [61] [156] [21]. However, it is challenging to improve animal
production efficiency and product quality under limited natural resources
(e.g., fresh water and land), thus precision production is critical for
addressing the issue. A key task of precision poultry production is monitoring
animal behaviors for the evaluation of welfare and health [41][49][31].
Application of AGI such as GPT-4 for analyzing poultry videos and images
requires innovation in the development of computer vision or machine vision
system [156]. Vision transformer (ViT) has become one of the emerging methods
for monitoring animal information and as a decision support tool in precision
animal farming [95, 101]. Previous works in poultry welfare analysis primarily
focused on using computer vision techniques such as convolutional neural
networks (CNNs) and image classification algorithms to detect birds’ feeding,
drinking, or preening behaviors [89][61] [157]. In recent years, advanced
methods were reported in tracking chickens’ problematic behaviors such as
pecking, mortality, floor distribution in broiler houses, and spatial
distribution in cage-free hen houses [62][185] [184]. However, monitoring
poultry welfare and production with computer vision is challenging due to
scarce of training poultry videos, labels of specific problematic welfare
issues such as lameness in broilers and pecking damages in layers, and
interpretation of analysis results to understandable language to poultry
farmers. Previous methods are not applicable for poultry farming as growers
usually don’t have knowledge of data analysis with ML or DL models. The
emergence of ChatGPT or GPT-4 provides a new capacity for farmers as it would
be not complicated to upload a chicken video or images into ChatGPT or GPT-4
as inputs and expect the outputs (e.g., specific poultry behavior or welfare)
from ChatGPT or GPT-4 automatically (see Fig. 5).

###  6.3 AGI for Agricultural Infrastructure

Infrastructure management and facility training are of critical importance to
the success and resilience in today’s agricultural sector [129]. Adequate
infrastructure support, such as bridges, roads, irrigation, storage, IT, etc
can greatly improve productivity, processing capability, and market values.
Due to infrastructure modernization, the complexity for managing and training
of this infrastructure grows exponentially, which makes traditional approaches
infeasible. In recent years, we have witnessed the potential of AI and AGI
tools for efficient processing and management of complex systems, which can
extend to the agricultural field [7, 116, 19, 42]. In this section, we will
summarize and envision the role of AGI in agricultural infrastructure
management and education.

####  6.3.1 AGI for Efficient Infrastructure Management

Reliable infrastructure is one of the key factors for ranchers and farmers to
expand operations and deliver better products [121]. Obviously, such
infrastructure has the potential to transform and modernize traditional
agriculture and farming into a more efficient, sustainable, and dynamic
farming system. It is a critical sector in terms of economic growth and
development and sustainable food production. Specifically, infrastructure
management includes agricultural roads, land development, and other
transportation infrastructure; water and irrigation, information systems,
agricultural equipment, machinery, building infrastructure (some examples
include greenhouses, warehouses, small industries connected with agricultural
production, silos, tanks, etc.), energy production for/from agriculture,
institutional infrastructure (agricultural research, extension & education
technology, information and communication services, financial services,
marketing, etc.), and agricultural waste management systems, etc. [113].

Management of these infrastructures, however, is a challenging task. It
requires coordination, proper allocation, and most importantly, efficient
utilization. Existing approaches rely mostly on human intervention and
scheduling, which cannot keep up with the scale of complexity. Therefore,
future infrastructure management will rely on advanced ML tools [76], such as
AGI, to remedy this issue. AGI can deliver the following innovative
infrastructure management tasks.

  * •

Predictive Maintenance. AGI can be used to analyze data from sensors and other
sources to predict when infrastructure components such as irrigation systems,
farm machinery, and storage facilities are likely to require maintenance or
repairs [9, 81]. This can help farmers and infrastructure managers take
preventive measures, reducing the risk of equipment failure and downtime.

  * •

Resource Optimization. AGI can help farmers and ranchers optimize the use of
resources such as irrigation, fertilizer, and pesticides. By analyzing data
from sensors and other sources, machine learning tools can help farmers
determine the optimal timing and amount of resource application, reducing
waste and increasing efficiency [43]. Besides, these tools can also be
efficiently coordinated for better utilization in terms of capital cost or
time-sharing values [112, 1].

  * •

Infrastructure Design and Planning. AGI can be used to analyze data from
weather sensors, soil sensors, and other sources to inform the planning and
design of agricultural infrastructure such as irrigation systems, drainage
systems, storage facilities, and other sensors [105, 1]. This can help
infrastructure managers optimize infrastructure design and improve facility
efficiency.

As one example, poultry infrastructure shown in Fig. 5 can be accelerated by
AGI towards more efficient management. Modern poultry house equips with
advanced vision sensors (such as RGB and infrared cameras), fans, air quality
monitoring devices, etc. With AGI data analytics and extraction, device
maintenance will be performed more precisely, such that downtime is minimized.
On the other hand, AGI can help optimize when and where to collect data from
whom, for better device resource optimization.

####  6.3.2 AGI for Infrastructure Training and Education

AGI can also help develop tools that can facilitate agriculture training and
education. The diverse types of equipment/devices make training and education
very challenging, time-consuming, and hard to generalize. Recently, the
development of AGI can relieve such challenges. For example, the proliferation
of learning tools has proven to be very effective in education and its
feedback evaluation. In this line of research, we expect to see innovative
tools, based on the principle of AGI, that can be utilized in the loop of
infrastructure training and education.

AGI can help infrastructural training and education in the following aspects.

  * •

Personalized Training and Education. AGI can be used to analyze trainee data
and provide personalized training recommendations, allowing trainees to focus
on areas where they need the most improvement [35, 192]. Besides, depending on
trainee’s background, a customized learning pathway can be designed, which
aims for better learning efficiency.

  * •

Predictive Analytics and Feedback. AGI algorithms can be used to analyze data
from training sessions to identify trends and predict training outcomes [6].
It is especially important since more training and education session now move
to online and the feedback are not easily sensible from the instructor’s
perspective. With advanced analytical tool provided by ML, it can help
trainers optimize training programs and improve training effectiveness [119,
12, 92, 190].

  * •

Continuous Learning. AGI can be used to create continuous learning programs,
providing farmers and ranchers with ongoing training and support even after
the initial training program has ended. This can be very helpful as farmers
and ranchers can receive life-long learning based on their progress, the
development of new equipment, or their farming type [115]. Compared with
current approaches, this new method will be very effective, especially
combining personalized learning and predictive feedback.

##  7 Conclusion

AGI has great potential to revolutionize agricultural and food systems,
including precision farming for crops and livestock, agriculture image
processing, NLP for agriculture problem understanding and answering,
agricultural robotics, agriculture and food knowledge graph, and agricultural
infrastructure. AGI can provide farmers and agricultural professionals with
valuable insights and advice on how to improve their productivity and
sustainability, while also addressing the challenges facing the industry, such
as climate change, food security, and rural development. AGI could have a
significant impact on farm automation, where robots and drones equipped with
advanced computer vision and machine learning capabilities could perform a
range of tasks, from planting and harvesting to monitoring crops and
livestock. With its ability to understand natural language, images, and
generate human-like responses and enhance robotics capabilities, AGI would be
an innovative tool for advancing agriculture and improving the livelihoods of
people involved in this vital sector.

This article mainly summarizes the possible applications and effects of AGI in
the domain of agriculture. However, the potential applications of AGI in
agriculture are virtually limitless, and the industry is just beginning to
scratch the surface of what is possible. Some of the other potential
applications of AGI in agriculture include precision farming, where advanced
algorithms can analyze data from sensors and other sources to optimize crop
yields, reduce waste, and minimize environmental impact. AGI can also be used
in developing new crop varieties through computational breeding, which could
speed up the process of developing more resilient, high-yield crops. The
potential applications of AGI in agriculture are vast and varied, and there is
no doubt that as the technology continues to evolve, we will see many exciting
new developments in this field.

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