[1]Deanna Flynn

1]Collaborative Robotics and Intelligent Systems Institute, Oregon State
University, P.O. Box 1212, Corvallis, 43017-6221, Oregon, USA

# Uncovering implementable dormant pruning decisions from three different
stakeholder perspectives

<EMAIL_ADDRESS>Abhinav Jain<EMAIL_ADDRESS>Heather Knight
<EMAIL_ADDRESS>Cristina G. Wilson<EMAIL_ADDRESS>Cindy
Grimm<EMAIL_ADDRESS>[

###### Abstract

Dormant pruning, or the removal of unproductive portions of a tree while a
tree is not actively growing, is an important orchard task to help maintain
yield, requiring years to build expertise. Because of long training periods
and an increasing labor shortage in agricultural jobs, pruning could benefit
from robotic automation. These efforts need not replace skilled human labor,
but may instead reduce the overall need for human labor by letting robot
pruners do significant starting cuts before human pruners finish the job.
However, to program robots to prune branches, we first need to understand how
pruning decisions are made, and what variables in the environment (e.g.,
branch size and thickness) we need to capture. Working directly with three
pruning stakeholders — horticulturists, growers, and pruners — we find that
each group of human experts approaches pruning decision-making differently. To
capture this knowledge, we present three studies and two extracted pruning
protocols from field work conducted in Prosser, Washington in January 2022 and
2023. We interviewed six stakeholders (two in each group) and observed pruning
across three cultivars — Bing Cherries, Envy Apples, and Jazz Apples — and two
tree architectures — Upright Fruiting Offshoot and V-Trellis. Leveraging
participant interviews and video data, this analysis uses grounded coding to
extract pruning terminology, discover horticultural contexts that influence
pruning decisions, and find implementable pruning heuristics for autonomous
systems. The results include a validated terminology set, which we offer for
use by both pruning stakeholders and roboticists, to communicate general
pruning concepts and heuristics. The results also highlight seven pruning
heuristics utilizing this terminology set that would be relevant for use by
future autonomous robot pruning systems, and characterize three discovered
horticultural contexts (i.e., environmental management, crop-load management,
and replacement wood) across all three cultivars.

###### keywords:

dormant pruning, pruning heuristics, grounded theory, pruning stakeholders,
planar architecture, critical decision analysis

##  1 Introduction

Dormant pruning in orchards consist of the removal of unproductive portions of
a tree or plant to maximize fruit production and growth stability over time.
Pruning encourages new growth, manages sunlight distribution throughout a
tree, and controls fruit productivity, quality, and placement (Alrigiwirsah et
al [3], Carroll [7], Kolmanič et al [21]). The time and skill required to
prune makes dormant pruning the second-most labor intensive and time-consuming
agricultural jobs in orchards, typically lasting from mid-November to mid-
April (Economy [13], He and Schupp [16], Gallardo et al [15]). The ability of
growers to hire and train enough workers to prune has worsened in recent years
given a 75% decrease in immigration farm labor compared to an 8% increase in
farm labor wages (Economy [13], Ag America Lending [1], Daniels [11]). This
labor shortage has led to an interest in automating dormant pruning, but
researchers must overcome several barriers before a robotic pruning system can
be effectively deployed.

The primary challenge to automated pruning, besides mechanical design and
perception, is how to make the decision of where to cut. Pruning is plant and
cultivar-specific and depends on many variables. Example variables include:
the type and age of cultivar, previous growth and crop-yield behavior, tree
architecture (e.g., V-trellis or vertical wall), the current and potential
environmental conditions, and desired crop-load and quality. How pruners weigh
these variables to produce cut decisions for a specific branch is unknown.
Moreover, these pruning decisions –— in practice –— are intuitive, relying on
visual and spatial heuristics learned from repetition and experiential
learning. Therefore, automating pruning decisions will require making these
intuitive and implicit decision processes explicit and algorithmically
representable.

As part of the study presented in this paper, our analysis has identified
three key stakeholders –— horticulturists, growers, and pruners —– each of
whom are responsible for different parts of the pruning decision-making
process (see Figure 1). It is not possible to fully automate pruning without
understanding all three perspectives, how each stakeholder acquires their
pruning knowledge, and how to communicate with each stakeholder.
Horticulturists have a holistic understanding acquired through formalized
studies that look at how particular actions affect the tree over the seasons.
For example, they might study how a tree’s cycle reacts to particular pruning
cuts (e.g., Kupka [23], Persello et al [28]), how the overall flow of nitrogen
throughout the tree affects growth (Kaba and Abunyewa [18]), or how the tree
progresses from bloom to blossom to fruit (Zhang et al [44]). In comparison,
growers (or crew managers) have more hands-on, experience-based knowledge
about the link between pruning and tree growth response, gathered from years
of experience with large orchards. Growers also interface between the
horticulturists and pruners. They distill their in-field experience, plus
information provided by horticulturalists, into heuristics that are doable and
fast to teach, but still yield quality fruit production with minimal labor.
Finally, the actual pruners focus on performing physical cuts as fast as
possible (about 1 to 2 cuts per second) since they are often paid by tree or
have target pruning goals set by growers.

Figure 1: Dormant pruning has three major stakeholders: horticulturists,
growers, and pruners. Each stakeholder interacts with orchards in a different
way (formal studies, crop management, paid per-cut work) and possesses unique
pruning knowledge acquired by formal techniques, hands-on experience, or
repetition.

To better disentangle the complex interplay between these stakeholders and
pruning decisions, we conducted three studies with six pruning stakeholders.
From these studies we extracted pruning terminology, decisions that affect
pruning, and heuristics across three different tree cultivars — Bing Cherries,
Envy Apples, and Jazz Apples — and two architectures (UFO, V-trellis). The
first study was an in-field, in-person, formative study conducted in January
2022. It consisted of two structured stakeholder interviews along with two in-
field pruning protocols. These protocols involved observing and interacting
with people who were pruning Bing Cherries (UFO) and Envy Apples (V-trellis).
The second study was an informal grower interview discussing example pruning
decisions made on 28 Jazz Apple trees (V-trellis) that were manually marked
with every cut labeled by type. Finally, our third study was a two-hour
virtual, discussion-based interview validating pruning terminology and
heuristics with two horticulturists.

Our analysis of these three studies is divided into two sections. The first
section provides the set of pruning terminology that the stakeholders use to
communicate their pruning heuristics. The second section reports the
underlying organization we extracted from the interviews; namely, global
goals, mid-level heuristics used to implement those goals in practice, and,
finally, the actual pruning cuts that arise from those heuristics. This
analysis clearly identifies the characteristics of a tree the automated system
must be able to identify, and provides a vocabulary and variables that are
important for communicating an automated pruning decision back to the
stakeholders (i.e., why the system chose to make the cut it did).

The remainder of this paper is outlined as follows. Section 2 reports other
successful automated pruning systems, their implementation gaps, and
documented pruning heuristics utilized in these systems. In section 3, we
describe the three study designs for extracting and validating pruning
terminology and heuristics. Our discovered pruning terminologies are in
section 4.1, and example heuristics identified for the two different tree
architectures (UFO and V-trellis) and three cultivars (Bing cherry, Jazz and
Envy apples) in section 4.2. Finally, section 5 discusses how future
researchers can utilize this study methodology in other pruning contexts, and
how to translate the discovered terminology and heuristics into an automated
robotic pruning system.

Our paper has three main contributions:

  1. 1.

An in-person interview protocol that mixes in-the-field interviews with
pruning observations in order to ground verbal descriptions with tree
morphology.

  2. 2.

A pruning terminology set, grounded in images and interactions with physical
trees, for communicating with stakeholders about pruning heuristics and goals.

  3. 3.

Organization of the information into a hierarchy reflecting global goals
linked to heuristics linked to physical cuts, in a form amenable to
algorithmic implementation.

##  2 Related Work

Pruning instructions/guidelines differ in their degree of ambiguity. An
example of an unambiguous pruning rule is: “remove any side branch longer than
5 inches”. In contrast, a pruning heuristic requires more human
interpretation, for example, “remove vigorous growth longer than 4 inches that
is shading other branches”. In the following discussion we use the terms
“rule” and “heuristic” to distinguish between the two.

The majority of existing pruning information comes in the form of heuristics —
exemplars that visually represent a final horticultural goal state of pruning,
as seen in pruning guides and pamphlets (e.g., Polomski [29], Maughan et al
[25]). However, people’s ability to use these guides and apply them to their
own trees is difficult because of the differences between their tree(s) and
the idealized before and after exemplars. Determining which pruning cuts on
their own tree will result in a tree that is “close” to the exemplar requires
expertise. This expertise is developed by learning (through experience) rules
and heuristics that transform an existing tree into the desired exemplar.

Previous literature provide pruning rules and heuristics for limb renewal for
various apple cultivars with a Tall Spindle architecture. For example, Robin
et al. (2008) report a pruning rule for ‘McIntosh’ or ‘Honeycrisp’ apples as
“removing branches that were upright” when the cultivar is 2-4 years old
(Robinson et al [31]). Likewise, Schupp et al. (2017) define a mathematically-
based pruning heuristic for ‘Buckeye Gala’ apples by pruning branches in
decreasing order of branch circumference size until a desired Limb to Trunk
Ratio (LTR) — the ratio of branch size to branch spacing — is achieved (Schupp
et al [34, 35]). To use this heuristic, the pruner can decide what order to
remove branches that have the same branch diameter to reach the desired LTR.

To obtain pruning rules and heuristics from experts — so that they may, in
turn, be implemented in autonomous pruning systems — previous researchers have
utilized at least one of two traditional methods. These are i) observing the
manual pruning process and identifying cut behaviors by visually comparing
trees before and after pruning (e.g., Poni et al [30], Karkee et al [19]) or
ii) discussing with experts about pruning strategies (You et al [43]). Once
developed, researchers integrate these rules and heuristics (simple and
complex) in autonomous pruning systems and test by validating cuts against
experts, either qualitatively or quantitatively. Qualitative validation asks
experts to label the system’s cut decisions as being correct or incorrect
(Corbett-Davies et al [10]). Quantitative validation, however, compares the
branch removal percentage for one tree or the percent similarity in pruning
cuts between the autonomous system and experts (Westling et al [40]). We
utilize both pruning observations and qualitative validation in our study to
ensure we do not make any a priori assumptions about pruning heuristics for
certain tree cultivars and architectures and to ensure the heuristics are
feasible for both autonomous systems and people.

Past autonomous pruning systems with successful implementations of formalized
pruning rules and heuristics is dependent on converting these rules and
heuristics into implementable algorithms. Implementing “simple” pruning rules
and heuristics require little to no modification by researchers. For example,
one woody structure that has a simple enough pruning rule to develop and
automate is pruning grapevines for light distribution. This is a hedging rule
— cut all branches to a specified length relative to the vine’s primary
cordon/wire support (e.g., Poni et al [30], Botterill et al [4], Sevila [36]).
However, implementation failures occur when there is a disconnect between the
goal-state exemplar and the visual-spatial tasks the machine must perform
(i.e., where and how to cut a branch). These failures happen more frequently
in orchards with more intricate tree architectures and complex rules (like
apple orchards with V-trellis or Tall Spindle architectures). This is due to
the fact that the cuts are precise in their placement and rely on multiple
tree factors and global objectives (like branch direction or crop-yield
expectations (He and Schupp [16], Akbar et al [2])). On the other hand,
implementing “complex” pruning rules and heuristics requires converting
pruning decisions — connecting global orchard objectives to individual cuts —
into quantifiable metrics for evaluation. For example, Westling et al (2011)
addressed the connection between pruning and light distribution throughout an
avocado and mango tree canopy (Westling et al [40]). Their system used LiDAR
tree scans to select and remove branches negatively impacting light
distribution. Their method achieved a 25% improvement in light distribution
compared to a 16% over commercial pruning. Other researchers have explored
where to prune based on 3D topological models of grapefruit vines (Corbett-
Davies et al [10]). This system chooses prunes by doing a brute force search
over all pruning schemes on the topological model and selecting the best based
on a defined set of 12 tree features. While this method was successful in
pruning similar to or better than a human, it is only applicable where brute
force search is feasible, such as on cultivars with similar architectures like
grapevines. For “complex” rules and heuristics, implementation failures arise
when the quantification metrics used by the autonomous system are different
than how the people quantify the same pruning decisions. This disconnect often
leads to the autonomous system performing invalid or unusable cuts as
validated by experts. Our paper explores ways to quantify pruning decisions
that are consistent between people and autonomous pruning systems.

Figure 2: Breakdown of our three studies based on participant ID and
procedure. Participant IDs reflect the person’s role, so P is a pruner, G is a
grower, and H is a horticulturist. We report how long each participant
participated in a specific study procedure and what cultivar and tree
architecture (either Upright Fruiting Offshoot or V-Trellis) they interacted
with.

##  3 Pruning Study Methodology: Learning in the Field

From informal conversations with experts in automation and horticulture, we
knew that we would need to interview three types of participants:
horticulturists, growers, and pruners. Additionally, we selected two pruning
systems a priori, based on the participants we had access to (UFO cherry and
V-trellis apple). When designing the interview protocol we had the following
concerns. First, we did not want to make any a priori assumptions about what
participants deemed “relevant” concepts, their terminology, or what went into
their decision process before, during, or after pruning. Second, we needed to
ground the discussion in the physical tree morphology so we could link terms
with actual exemplars. Third — and somewhat in conflict with the first concern
— we needed to ensure that we captured all relevant information while in the
field. Finally, we wanted participants (particularly the pruners) to prune
normally and not “overthink” it.

We conducted three studies over the course of one year (see Figure 2). The
first study was an in-field study consisting of a structured interview process
largely following the critical decision approach (Klein et al [20]) — an
interview protocol that focuses on identifying decision points and using
follow-up questions to elucidate why participants make the decisions they do.
We used grounded coding to analyze that study. Our second “study” took
advantage of a very unique opportunity — 28 trees manually labeled over 3 days
by a grower and his senior pruner. This study included an ad-hoc interview
with the grower after the labeling was complete. Our final study was a follow-
up study to validate the analysis of the first two studies.

We first briefly describe the methodology, participants, and protocols used in
each study. The first sub-section (Section 3.4) describes what data we
collected in our interviews and how we applied grounded coding in general to
that data. The following three sub-sections contain details of each study and
how grounded coding was applied to those studies.

In-field study design (Study 1): This study is an in-field, question-driven
protocol with two pruning protocol variations: silent and speak-aloud. We
first explained the purpose and style of the study and collected information
about the participant. We emphasized the importance of demonstrating, where
possible, the concepts, terms, and cuts on the actual tree. For the speak-
aloud protocol, we worked through the questions given in Table 1, prompting
follow-up explanations and demonstrations where necessary. For the silent
protocol, we simply had the pruners perform cuts as they would normally. This
protocol style reflects an ethnographic, fly-on-the-wall observation
(e.g.,Chun and Knight [9], Fallatah et al [14], Silverman et al [37], Rothwell
et al [33]). We had originally intended to do a retrospective think-aloud with
the pruners but decided not to after witnessing the scale of pruning work (the
pruners were working in the field during the interview).

Key to our interview protocol was developing the list of questions along with
a “checklist” of additional information/details we wanted to collect for that
question. This checklist served as both prompts for follow-up questions and to
ensure we did not “miss” any data.

In-field (Study 2): This study is an ad-hoc, in-field study that was made
possible through the generous time and effort of one of our growers. The
grower, along with one of their most experienced pruners, manually labeled all
pruning cuts on 28 V-trellis, Jazz Apple trees (colored twist ties placed
around cut points simulating over 1,000 cuts). The grower was informally
interviewed by one of the team members, and the conversations were recorded
using a cell-phone. Additionally, the trees were fully scanned using both a
manual method (cell-phone) and a robotic platform (Azure RGB-D camera mounted
on a scanning platform). Since this study took place after the first study was
analyzed the interviewer was able to explicitly query the grower about what
tree features and high-level goals led to specific cuts.

Validation Interview (Study 3): Following the analysis of the first two
studies, we comprised a list of pruning terminology and a set of UFO and
V-trellis pruning heuristics. The key to this study was how we presented this
discovered pruning terminology and heuristics while encouraging discussion
between participants.

This study is a presentation-based, virtual discussion with two of our team’s
horticulturists (one being a participant in study 1). For each term, we
presented the term alongside the description and, if applicable, a visual
followed by the interviewer asking participants if the term was correct and
how to modify it. For each heuristic, we presented a mapping of how the branch
was growing and why it was cut (i.e., horticultural context) to specific cut
actions using images and video. The interviewer then asked the participants if
the heuristic was feasible to use given the context and how to modify the
mapping or actions to make the heuristics more usable by people.

###  3.1 Interview and video analysis (studies 1 & 2)

Our analysis aims to visually and verbally extract relevant pruning concepts,
identify what influences pruning decisions, and discover pruning heuristics by
tree cultivar. Furthermore, we wanted to avoid making a priori assumptions and
instead have the data to guide the analysis process.

We created an analysis technique (based on grounded coding) to extract pruning
decisions. Our analysis uses more than six hours of video data collected from
interviews and pruning observations with all audio converted to transcripts.
For all analyses, two researchers worked concurrently in a shared excel sheet
to extract and code transcript text and sections of video, and whenever
possible, link images that visually describe the transcript quotes.
Researchers resolved any disputes throughout this process by discussing why
the data satisfied the extraction criteria based on the type of analysis being
performed.

Our analysis is divided into two sections: pruning terminology (Section 4.1)
and heuristics (Section 4.2). For the pruning terminology, two researchers
extracted participant transcript quotes and video instances from all
interviews and pruning observations when i) the participant physically
identified a tree feature by gesturing (with hands or loppers); ii) the
participant demonstrated an action and provided a corresponding name; or iii)
the participant gave a definition to a corresponding term like “biennial
bearing is where it is very productive one year, and then those fruit(s)
inhibit flowers for the next year.” All terminology the researcher’s
identified was saved with an associated term (e.g., “bud”) and the definition
or term and video instance (when possible) in the shared excel sheet.

For the pruning heuristics analysis, the same researchers extracted video
clips where the participant either made a physical pruning cut or the tree had
a colored tag. Key to this analysis was, for each branch physically pruned or
tagged branch, we added generalized and cultivar-specific descriptions about
the branch (e.g., Branch Vector) in different columns representing different
tree characteristics (e.g., length) in a shared excel sheet. The labels and
corresponding descriptions were agreed upon by both researchers either before
or during the analysis. We also extracted sections of transcripts when
participants provided explanations about prunes or what influenced their
pruning decision. Finally, using the extracted videos and branch descriptions,
one researcher independently mapped the total number of cuts to various
groupings of generalized and cultivar-specific labels; the same labels used to
describe the branch when we extracted video clips. These mappings represent
pruning heuristics discovered from real-world cuts to the three cultivars, or
more specifically, the two tree architectures.

The remainder of this section is as follows: Subsection 3.2 details our
selection process of participants and cultivars. Subsection 3.3 details our
interview protocols and data collection methods for all three studies; the
formative study interview in Subsection 3.3.1 and pruning protocols in
Subsection 3.3.2; the informal grower interview procedure in Subsection 3.3.3;
and the validation interview in Section 3.3.4. Finally, Subsection 3.4 defines
our grounded coding analysis technique to identify pruning terminology and
heuristics.

###  3.2 Selection of Participants and Cultivars

Across all our studies, we received consent and interacted with six people,
two from each pruning stakeholder category (see Figure 2 “Participant ID”). We
recruited participants through established contacts of our research team’s
horticulturists. The horticulturists are members of our research team who
specialize in efficient orchard architectures and agricultural fruit
mechanization. The two growers are contacts of the horticulturists and
research group (WTFGA) who are interested in automation and modern techniques
in agriculture. Finally, the grower in the in-field formative study
(participant $ID=G1$) selected the two pruners on the day of our visit based
on pruning skill level.

We selected the cultivars based on guidance from our horticulturalist experts.
The Bing Cherry cultivar is from an affiliated university institute’s research
site and utilizes an Upright Fruiting Offshoot (UFO) architecture, co-
developed by the interviewed horticulturist (Whiting [42], Whiting et al
[41]). The UFO architecture was designed to be both easy to prune and to
harvest (Whiting et al [41], Du et al [12], Whiting [42], Lang et al [24]).
The Envy and Jazz Apple cultivars both employ the V-trellis architecture,
originally designed to simplify harvesting, with fruit easily reachable from a
moving vehicle (Robinson et al [32]). This architecture trades-off pruning
complexity for yield-control and harvesting speed. Although not originally
designed for automation, both architectures are essentially “planar”, making
it feasible to develop robotic pruning systems for them.

###  3.3 Interview Protocol and Data Collection

This subsection defines our interview procedures, pruning protocols, and data
collection methods for our in-field formative study, the informal grower
interview with tagged trees, and the pruning decision validation interview.
For all participant interactions, we captured video and audio recordings and
later transcribed them for grounded coding analysis.

####  3.3.1 Study 1: In-the-Field Interviews

The participant interview followed a semi-structured format and lasted
approximately one hour, with the first 45 minutes focused on pruning and the
remaining 15 minutes on robotic expectations and workflow. Initial questions
are given in Table 1. The questions address decision-making strategies,
pruning considerations and instructions, evaluation techniques, and preferred
robot interactions. Our team also asked follow-up questions to clarify
concepts or terminology throughout the interview and encouraged participants
and study members to point at the tree. Examples of follow-up question
structures are:

  1. 1.

What do you mean when you say [terminology]?

  2. 2.

Can you point to [terminology] on the tree?

  3. 3.

Can you demonstrate [pruning action]?

  4. 4.

Why did you make [pruning decision]?

  5. 5.

What influenced you to make/take [pruning decision/action]?

Our in-the-field research team consisted of five people (two interviewers,
three recorders), with two cameras and two microphones. The primary
interviewer asked the prompt and follow-up questions. The secondary
interviewer made note of which items on the check-list had been covered and
communicated with the primary interviewer if any check list items were
missing. We used a dedicated audio recording device (recorder 1) plus one Go-
Pro to record the entire scene from a tripod (recorder 2), while the second
Go-Pro was manually moved around to “zoom in” on any features the grower
mentioned/the pruners cut (recorder 3).

Participants and cultivars: We interviewed four participants —– one
horticulturist, one grower, and two pruners —– in Prosser, Washington, in
January 2022. The cultivars were Envy V-trellis apples and Bing cherries in an
Upright Fruiting Offshoot (UFO) architecture. Total interview time was
approximately four hours.

Relevant Pruning Concepts
---
\- What features do you use to determine age of a branch?
\- What is considered a healthy branch? What features were you looking
at/considering?
\- How do you determine a vegetative versus flowering bud?
Pruning Heuristics and Instructions
\- What instructions do you give/would you give to the pruners? \- Do the
instructions vary with tree age?
\- Do you have specific rules for pruning?
\- How specific do you need to be in giving instructions?
\- Do you ever watch someone prune and then provide additional instructions?
\- How much variation do you see in pruners? What does it look like?
\- What specifics about this block are you using to make your instructions?
Root stock? Prune type? Previous yields?
\- Does the age of the branch matter for pruning?
Pruning Decisions and Strategies
\- What do you consider when you choose a pruning strategy. What are you
thinking/making decisions about?
\- What is your process for deciding how to prune this block? How much of this
varies per block?
\- What are the goals of pruning? What are you trying to achieve by pruning?
\- Do you have a wider strategy for pruning?
\- Are there different types of pruning you might do?
Pruning Evaluation
\- How do you evaluate the resulting prune?
\- How do you evaluate a good versus bad prune?
\- How much evaluation of a good or bad prune can yon figure out from just
looking at the tree?
\- How much of evaluating a good or bad prune is determined by evaluating
later?
Robot Expectations and Preferred Interactions
\- How do you envision interacting with a robotic pruning system?
\- How would your pruners interact with a robotic system?
\- What do you think the robot would do [for pruning]?
\- How would you like to see prunes? Before and after trees (digital)? Showing
cuts before they’re made? Let the pruners spray paint marks (robot system
cuts)?
\- How comfortable are you with letting the robot make pruning decisions?
\- What would make you more comfortable with the robot? Seeing before and
after trees images (digital)? Seeing it do the cuts?
Table 1: List of questions utilized in the In-the-Field formative study
participant interviews described in Section 3.3.1

####  3.3.2 Silent and Speak-aloud Pruning Protocols

In order to observe pruning in the field, we developed two pruning protocols:
silent and speak-aloud. For the speak-aloud pruning protocol, the participant
demonstrated pruning while providing explanations, such as why they were
making that particular cut and their cut strategy. During this protocol, our
primary and secondary interviewer interjected questions to ensure all unknown
explanations and visual terms were grounded in actual tree morphology. In
contrast, for the silent pruning protocol, the participant pruned without
providing explanations and no interruption from our research team.

Participants and cultivars across both pruning protocols are the same as the
in-the-field interviews described above. The grower and horticulturist
utilized the speak-aloud protocol, while the two pruners used the silent
pruning protocol. We recorded a total of 13 minutes of video across both
protocols (silent: 8 minutes; speak-aloud: 5 minutes).

Figure 3: Example of a tagged tree with the four colored pruning tags. Each
colored tag corresponds to a different pruning action. Green: Fruit spacing,
Red: Self-shading or out-of-canopy growth.

####  3.3.3 Study 2: Informal grower interview with marked trees

Research members conducted an additional pruning decision interview in January
2023 with a grower and 28 tagged apple trees (see Figure 3). Tag placement was
a collaborative effort between the grower and an expert pruner. Different
pruning decisions were marked with red or green tags, white tags, white flags,
and orange dots. Red tags indicated pruning decisions to maintain the fruiting
structure, or limiting outward growth from the secondary branch between six
and eight inches. Green tags marked prunes for maintaining two inch fruit
spacing between buds. Likewise, white flags identified branches that were too
vigorous that need to be removed. White tags were branches that were too close
to the support branch and would produce shaded fruit or fruit that would rub
against the branch. Finally, orange dots indicated places within a six-inch
radius from the tag a pruner should avoid pruning so the tree can grow
replacement wood. This tagging represented highly detailed and precise pruning
decisions, far more than hired pruners would utilize “in practice” given time
constraints. Questions asked by the research team were largely around
clarifying tag placements and why that pruning cut corresponded to a specific
color. Additionally, the grower answered a series of questions about the
overall architecture (angle of trellis, spacing) and the 3-5 year process of
establishing the mature orchard. The questions are similar to those asked in
study 1 (see Table 1).

####  3.3.4 Study 3: Pruning Decision Validation Interview

One research member conducted a follow-up interview over Zoom with two
horticulturists (one being the horticulturist initially interviewed in study
1) to validate the extracted pruning terminology and heuristic for Upright
Fruiting Offshoot and V-trellis and further connect local pruning heuristics
to high-level horticultural concepts. The researcher, acting as the
interviewer and discussion moderator, oriented the horticulturists to the
discovered pruning decision structure in the first five minutes, followed by
twenty minutes presenting terminology and definitions alongside visual
examples for Cherry and Apple trees. For most of the interview time (i.e., 70
minutes), the interviewer presented individual pruning heuristics captured
from analysis of the first two studies (see Section 3.4.2) with accompanying
diagrams and real-world pruning examples (45 minutes for Apples, 25 for
Cherries). During the remaining time, the interviewer asked the
horticulturists to discuss pruning contexts that can influence pruning
decisions not currently addressed or considered and workflow and robotic
considerations.

Table 2 reports the questions asked by the interviewer based on what
information was being validated for both pruning terminology and heuristics.
The interviewer formulated each question to encourage discussion amongst the
interviewer and horticulturists about the validity of the pruning heuristics
and recommendations for improving the pruning decision by modifying labels and
terminology. We report our final pruning terminology set and discovered
pruning heuristics, incorporating any suggestions recommended during this
interview, in the following sections.

Pruning Terminology
---
\- Would you change the term definition in any way? If yes, how?
\- Is the definition used for the term accurate?
\- What other terms do you feel are missing from the list shown?
Pruning Decisions and Horticultural Goal to Heuristics Mapping
\- Is the current horticultural concept this heuristic mapped to
correct/accurate? Is there another concept you would add?
\- Does this mapping form Pruning Branch Labels to Physical Cut labels easy to
follow?
\- Would you modify this heuristic in any way? How? Why?
\- Is there a pruning context you feel is missing that would modify this
heuristic?
Pruning Contexts
\- What are other ways in which planters vary pruning to the particulars of
their fields? How much does that vary across particular people, crops, etc?
\- Are there any ways we can parameterize the rules more, i.e., allow for more
input by people?
\- Are there opportunities for parameterization, interfaces, algorithms to
assist in specifying these?
Workflow
\- Do you have any insights on how you might expect farmers to integrate
mechanical pruning and/or pruning interface customization/intelligence going
into the future?
\- In terms of information flow, what might be the role of managers… humans in
the field… co-labors?
\- Any other thoughts on interfaces or algorithms?
\- Anything else you wanted to say or that you hoped we would ask?
\- We think it would be really important to show growers (on a screen with
virtual trees or by painting branches) actual pruning cuts the system would
propose, rather than “rules” per-se. Does this make sense?
\- What “knobs” do you think growers would like to control with a pruning
interface? Number of buds? More or less vegetative growth?
Table 2: List of questions (grouped by discussion topic) utilized in the
validation interview described in 3.3.4

###  3.4 Grounded Coding Analysis Approach

In this subsection we define grounded coding, also called grounded theory
(Bryant and Charmaz [6], Pandit [27], Charmaz [8]). In the following
subsections we apply this approach to extract the pruning terminology
(Subsection 3.4.1) and heuristics (Subsection 3.4.2).

Grounded coding is a multi-step analysis technique widely used in various
social sciences, such as anthropology and psychology (Von Feigenblatt and
Acuña [39], Henwood and Pidgeon [17], Mohajan and Mohajan [26]), to analyze
semi-formal and informal interviews of subject experts. This analysis
technique and how we applied it consists of three steps:

  1. 1.

Extracting data: in this step we identified transcript quotes and video clips
that share a common trait relating to one or more of our research questions
and follow-up prompts.

  2. 2.

Open coding: in this step we add descriptive labels to the extracted data that
define the perceived connection between the data and research question(s).

  3. 3.

Axial coding: in this step we group subsets of labels created in step two to
capture holistic relationships for each research question.

Each of the three steps, and their connection to the targeted research
questions, is how the analysis stays “grounded” without having a priori
knowledge of the data. Instead, the open and axial coding process reveals
relevant concepts that researchers may not have identified or considered using
different analysis techniques.

For all analyses, two study members worked concurrently in a shared excel
sheet. The sheet had a dedicated column for referencing which transcript or
video the researcher extracted the text or visual data from and a column to
store the extracted data. We added additional columns in the data sheet to
help describe the extracted data, but these columns are dependent on the
analysis. For pruning terminology, one column was added for textual
definitions of the extracted terminology. For pruning heuristics, these
columns came from pruning terminology reported in section 4.1 that represent
visual tree contexts (e.g., tree geometry) and cut actions. Both researchers
resolved disputes in what and how to extract and label data by discussing
either how the data fit the extraction criteria (for pruning terminology) or
why the data matched the associated terminology description (for heuristics).
We report the exact implementation of grounded coding analysis for pruning
terminology and heuristics in the following subsections.

As a reminder, the extracted terminology and heuristics were further
validated/refined in our third follow-up study.

####  3.4.1 Pruning Terminology Analysis

Due to the complexity of the data (visual and audio) and the need to link
definitions and descriptions to visuals, we used three passes for the
extracting data step of the grounded coding (visual, visual plus terminology,
and definitions). In all cases, two study members stored the resulting
extracted data as text and images in a shared excel sheet containing three
columns.

  1. 1.

Term: a keyword or phrase used to describe an action or object.

  2. 2.

Definition: a verbal description of the action’s or object’s appearance (e.g.,
general motion or shape) and purpose.

  3. 3.

Visual Cue: example image/video segment demonstrating the term.

The first pass method consisted of identifying and extracting sections of
transcripts and video where the interviewee physically pointed or gestured to
a visual tree feature with their hand or loppers while providing a
description. For example, when the horticulturalist identified a bud by
pointing to it and saying, “buds, they’re covered in frost here, but this is a
bud.”

The second pass extracted video and transcript quotes whenever a participant
demonstrated an action and provided a corresponding name. As an example, the
horticulturist (while describing a cut) would point or use their hands as
“scissors” to demonstrate where the lopper would be placed for a “thinning
cut”, as shown in Figure 4.

Figure 4: An example of how terminology was extracted from transcript quotes
and visual cues during the in-field interviews. The discovered term —
“thinning cut” — is represented in orange with its corresponding definition in
pink. Finally, an example visual representation of the cut explained and
demonstrated by the participant by pointing to the branch is shown in the left
side of the image labeled “Visual Cue” and the accompanying text in purple.

Finally, the third pass looked for quote instances when a participant provided
an accompanying definition or example descriptor to a corresponding term. For
example, “biennial bearing is where it is very productive one year, and then
those fruit(s) inhibit flowers for the next year”. This quote identified the
term “biennial bearing” and provided a definition for that term.

After extracting the data, the next step in the grounded coding process is
open coding. In this step, we created definitions using explanations and video
given by participants during interviews and pruning observations for each
term. When participants provided multiple explanations for a given term, both
researchers worked together to create a definition that captures common themes
across all provided explanations. For example, for the term “thinning cut” we
defined the corresponding definition as “a type of cut performed by a person
which removes a branch at its base.” The definitions, therefore, represented
our label set.

The final step is the axial coding. Here, we grouped each term and its
corresponding definition into sub-categories based on similar characteristics
such as tree features or physical cuts. For example, all terminology that
described the direction a branch was growing were all grouped together as a
Branch Vector. Lastly, two study members grouped discovered sub-categories
into hierarchical levels based on contextual and visual inputs a robotic
pruning system would need and physical outputs or actions the robots might
perform. We provide the final set of terminology and the hierarchical levels
in Section 4.1, and in Tables 3–6.

####  3.4.2 Pruning Heuristics Analysis

We analyzed participants’ pruning processes in two phases: i) extracting and
labeling physical pruning cuts from video of the two pruning protocols,
informal interviews with marked trees, and in-the-field interviews, and ii)
constructing heuristics for each cultivar based on how participants explained
their pruning decisions.

For extracting and labelling pruning data, two researchers recorded all data
as text in a shared excel sheet. This excel sheet contained 17 qualitative
codes (one code per column) that recorded the data’s origin (i.e., which video
and the timestamp) and characteristics of the branch, such as the number of
buds, Branch Length, or Branch Vector. All columns — and for which tree
cultivars they were applicable to — were agreed upon by both researchers
before or during the analysis process. Furthermore, for certain qualitative
codes, researchers also agreed on the associated visual cues. For example, for
the Branch Vector column, researchers added one of six agreed upon descriptors
— “Parallel to the Wire,” “Into the Row”, “Into the Interior”, “Out of the
Row”, “Up from Wire”, and “Down from Wire” — based on the tree architecture.
We report the set of columns and their associated descriptions in Section 4.1.

####  3.4.3 Extracting and Labelling Pruning Data

Our extracting data step consisted of finding video segments where there were
instances of either a physical pruning cut — the participant’s loppers came in
contact with a branch and that branch was cut — or tagged branches — branches
visibly marked with at least one colored tag defined in Section 3.3.3. When
either researcher identified a physical prune or tagged branch, it was
documented in the excel sheet with which video (video name) and the time stamp
of when the prune or tag happened. All instances identified by both
researchers represent individual rows in the shared excel sheet.

For the open coding step, one researcher filled in visual cues in the
associated qualitative code (column) for each extracted data instance (row).
We provide an example of open coding labelling with a subset of columns in
Figure 5. The qualitative code was left blank in the excel sheet if it was not
relevant for that cultivar’s architecture. For example, we only used Observed
Wire Number when describing cultivars with the V-trellis architecture (Apples)
and not for Upright Fruiting Offshoot (Cherries).

Figure 5: An example of our extracting data and open coding step for pruning
heuristic analysis. We extracted a branch from a Jazz Apple video segment due
to the presence of a colored tag (red). The extracted branch was added as a
new in the excel sheet and visual cues filled in by one researcher in the
appropriate columns for that cultivar’s architecture (Apples: V-Trellis;
Cherries: UFO).

Finally, our axial coding step involved moving excel columns left or right
based on label groupings and order of relevance. We report the complete set of
qualitative labels (terminology and visual cues) for each cultivar in Section
4.1 (see Tables 3–6.).

####  3.4.4 Constructing Heuristics

To construct heuristics for each tree cultivar, two study members analyzed
participant explanations provided during pruning observations and in-the-field
interviews to discover connections between qualitative codes. Specifically, we
rewrote explanations with the corresponding qualitative code or terminology
sub-categories. Figure 6 shows an example mapping of a participant quote to
terminology categories and the corresponding visual cue as shown in purple and
blue. The pruning explanation mappings provided a starting point for
performing an analysis to map input labels to output labels. It also helped
identify the global context or horticultural goal of why the participant made
a cut (shown in red) and which labels were more or less critical, unnecessary,
or redundant for each tree cultivar and pruning decision.

Figure 6: We utilized participant quotes to capture pruning heuristics syntax
by identifying terminology (excel columns) in the text.

Utilizing these discovered mappings, one study member numerically analyzed
pruning cuts by mapping the percentage of occurrences for specific input label
descriptors to output label descriptors in ranked order. The study member drew
lines connecting specific input-output label descriptors if a) every instance
of the input-output mapping (i.e., numerical analysis returning 100%) maps to
this decision or b) the input-output mapping returns the maximum percentage
after utilizing all labels. Section 4.2 reports the example heuristics,
ordered by cultivar.

##  4 Results

We present the results of our grounded coding analyses for pruning terminology
(Subsection 4.1) and heuristics (Subsection 4.2) in the following two
subsections.

For pruning terminology, we report the four hierarchical terminology
categories discovered from our analysis and the 16 associated terms,
definitions, and visual representations extracted from six hours of interview
and pruning observation video and audio transcripts. These terms and
definitions are used in formulating our heuristics by grouping visual-based
input tree features to action-based outputs.

For pruning heuristics, we report the three most common contexts that
influence pruning decisions. We also present a set of seven pruning heuristics
that map from these contexts to specific cut decisions categorized from 661
distinct physical pruning cuts and tagged tree instances (Bing Cherries,
N=124; Envy Apples, N=229; Jazz Apples, N=308) across five participants — two
growers, two pruners, and one horticulturist — over 13 minutes of pruning
observation video (Bing=5 min, Envy=6 min, Jazz=2 min). Finally, we discuss
four pruning visual-spatial tasks participants use when pruning and how they
affect cut decisions.

###  4.1 Results: Pruning Terminology and Definitions

In this section we present the terminology and definitions that were extracted
from the raw interview footage (Section 3.4.1) and refined in the Validation
study (Section 3.3.4). In the axial coding step these terms were organized
into a hierarchy four levels deep with four top-level categories. We present
each of these four categories in turn, along with their sub-category terms.

Label |  Defintion |  Example
---|---|---
Tree Cultivar |  The biological cultivar of the tree |  Envy Apple
Tree Architecture |  Verbal description of the current plant architecture of the tree |  Upright Fruiting Offshoot (UFO)
Root Stock |  The biological classification of the tree’s root structure |  Algae-41
Table 3: Tree Context has three corresponding terminology — tree cultivar,
tree architecture, and root stock — that represent high-level horticultural
concepts and knowledge of the tree cultivar being pruned. We provide
definitions and example classifications for each term.

####  4.1.1 Tree Contexts

This category contains general terms for high-level horticultural concepts and
information about the tree cultivar. These terms do not directly influence
individual pruning decisions. However, they do specify how specific tree
cultivar characteristics influence high-level pruning goals. Table 3 shows the
three terms at the next level of the hierarchy along with their definitions

Tree Cultivar refers to the biological classification of the cultivar. Our
study included three different tree cultivars: Bing Cherries, Envy Apples, and
Jazz Apples.

Tree Architecture refers to the architecture of the tree. Our study included
two tree architectures: Upright Fruiting Offshoots (UFO) and V-trellis
(apples). Figure 2 shows an abstract representation of these two
architectures.

Rootstock refers to the biological classification of the tree’s root stock
(the cultivar below the graft union). In our studies, the apple trees used
Algae-41 for the apple root stock, which is a dwarf root stock.

Label |  Cultivar |  Definition |  Example Label
---|---|---|---
Tertiary Branch Type |  General |  Biological classification of the tertiary branch, e.g., whether it will grow fruit in cherries |  Lateral Spur Wood, Lateral Vegetative Wood
Branch Length |  General |  An estimation of the length of the branch given in inches before pruning |  e.g, 8, 10, 14
Branch Vector |  General |  The growth direction of the branch relative to the main branch or overall plant structure |  e.g., Into the interior, Parallel to wire
Branch Vigor |  General |  A description of the vigor of the current branch |  Weak, Strong
Type of (Most Common) Bud |  Cherry |  A description of the most prevalent bud on the branch being pruned |  Fruiting Spur, Vegetative
Number Fruiting Spurs before/after cut |  Cherry |  The total number of fruiting spur buds present on the branch before/after pruning |  e.g., 2, 4, 5
Number Buds before/after cut |  Apple |  The total number of buds present on the branch before/after pruning |  e.g., 0, 1, 2
Observed Wire Number |  Apple |  Numerical representation of the wire number closest to the branch |  e.g., 1, 2, 3
Table 4: There are seven pruning branch labels that represent different tree
features that influence pruning decisions. These labels can be divided into
two categories: generalized and cultivar-specific. Generalized labels are
labels that apply to all three tree cultivars (i.e., Bing Cherry, Envy Apple,
and Jazz Apple) and is represented by “General” in the Cultivar column, while
the cultivar-specific only apply to one or two.

####  4.1.2 Pruning Branch Terms

This category contains eight terms that define either geometric properties of
the branch(es) or biological ones. These properties are the ones that are used
to identify which branches to prune, and where to prune them. Four of these
terms were used in all cultivars, while the other four were used when
discussing a specific cultivar (apple or cherry). This reflects the tree
biology and architecture. 111In cherries fruiting versus vegetative buds are
distinct, while in apples a bud may contain a mix of leaves and flowers. The
terms are summarized in Table 4.

Tertiary Branch Type [General] refers to the biological classification of the
tertiary branch. In Bing Cherries, there were two tertiary branch Types:
“Lateral Spur Wood” and “Lateral Vegetative Wood”. “Lateral Spur Wood” refers
to starburst-shaped buds which will produce cherries, while “Lateral
Vegetative Wood” produces new branches (see Figure 7, top row).

Envy and Jazz Apples also possessed two tertiary branch types: “Lateral Wood”
and “Spur” (see Figure 7, bottom row). These branch types differ in length.
“Spur” tertiary branches are typically short (less than 4 inches), while
“Lateral Wood” is longer with fewer buds.

Figure 7: Bing Cherries possess two tertiary branch types: Lateral Spur Wood
and Lateral Vegetative Wood. “Lateral Spur Wood” are branches that will
produce cherries, while “Lateral Vegetative Wood” will grow new branches.
These branch types are distinguishable by the shape of the end bud. Fruiting
Spur Buds are shaped like starbursts while vegetative buds are singular
spikes. Envy and Jazz Apples also possess two branch types — “Lateral Wood”
and “Spurs” — distinguishable by branch length (Spurs $\leq$ 4 inches, Lateral
Wood $>$4 inches). Both branch types possess spike-shaped buds which can be
either fruiting, vegetative, or reproductive.

Branch Length [General] refers to the total length of a branch from its base
to the tip. The value associated with this term is typically a numerical
approximation since directly measuring each branch is not feasible in
practice. Length of a branch is one feature used to identify which branch to
remove.

Branch Vector [General] refers to the growth direction of a branch relative to
the main branch or architecture/overall plant structure. The value associated
with this term is typically qualitative. This angle is another feature used to
define which branch to remove.

The Upright Fruiting Offshoot (UFO) for Bing Cherries have four main
qualitative branch vectors (see Figure 8 “UFO”): “Into the Interior,” “Into
the Row,” “Parallel to the Wire,” and “Out of the Row.” “Into the Interior”
and “Into the Row” are branches growing into or out of the plane defined by
the UFO’s wire structure. “Parallel to the Wire” are branches growing in the
plane and parallel to the (horizontal) wires. Lastly, any other branches that
do not satisfy other vector classifications are “Out of the Row.”

The V-Trellis Architecture used on the Envy and Jazz Apples have five main
branch vectors (see Figure 8 “V-Trellis”): “Into the Interior”, “Into the
Row”, “Parallel to the Wire”, “Up from the Wire”, and “Down from Wire”. The
first three terms have the same descriptions as above. “Up from Wire” and
“Down from Wire” grow parallel to the planar tree architecture and wire, but
point up (or down).

Figure 8: Tertiary branches refer to the small branches that produce fruit and
leaves. These branches grow in different directions relative to the plane
defined by the support posts and wires. In addition to length, qualitative
terms were used to define the direction of growth of the tertiary branch
relative to this support structure. The UFO architecture utilized four
different terms, distinguishing between in and out of the plane and being
parallel to the wire. The V-trellis architecture extended these terms to more
specifically indicate the growth direction in the plane (up or down).

Branch Vigor [General] is a qualitative term that ranks the branch’s growth on
a scale from “Weak” to “Strong”. Although somewhat subjective, vigor is
determined by i) comparing the thickness of the tertiary branch to the support
branch it grows out of, ii) the spacing of the buds on the branch, iii) the
length of the branch, and iv) the color of the wood . The thicker the tertiary
branch, the more vigor it has. Figure 9 provides examples of both “Strong” and
“Weak” vigor for all three tree cultivars.

Figure 9: The term Vigor refers to how vigorous a tertiary branch is growing
and is represented by a scale from weak to strong. Determining where a branch
lies on the scale is a complex combination of length, diameter relative to the
secondary branch, bud spacing and (sometimes) direction of growth. For
example, Bing Cherry tertiary branches with strong vigor are often thicker,
longer, have widely spaced buds, are shiny, and vertically. In contrast, weak
vigor branches are shorter, thinner, and droop.

Type of (Most Common) Bud [Cherry] refers to the ratio of fruiting versus
vegetative bud on a tertiary branch. This term is a partial extension of the
Type of Branch term as branches can possess both “Fruiting Spur” or
“Vegetative” buds; a branch is designated a “Fruiting spur” or “Vegetative”
depending on which bud type the branch has more of.

Number of Fruiting Spurs Before/After Cut [Cherry] and Number of Buds
Before/After Cut [Apple] terms refer to the estimated number of (fruiting
spur) bud sites that exist before or after pruning. This is a numeric count of
the number of buds along a branch (usually a tertiary branch). In the Bing
cherry, fruiting buds (versus vegetative) are easily distinguished while in
apples this is not always the case (and buds produce both leaves and fruit).

Observed Wire Number [Apple] refers to which wire is closest to the pruned
branch. Observed wire numbers start at 1 at the bottom and increase as they go
up; a typical V-trellis has 5-7 wires spaced 18 inches apart. Because

####  4.1.3 Pruning Explanations [Global/Local]

This category covers explanations for why a particular pruning cut was made
(see Table 5). These explanations can further be divided into Global versus
Local explanations. Each explanation also has an associated context (in the
form of a verbal description) of when and how that decision was made. These
explanations help connect specific pruning cuts to higher-level horticultural
concepts.

Label |  Definition |  Example Label
---|---|---
Global vs. Local |  Identification of the influence level of the pruning factors for the specific cut |  Gobal, Local
Pruning Context and Explanation: |  Verbal description of pruning explanations or context of the cut |  “Growing into the interior”
Table 5: Pruning Explanations [Global/Local] terminology consists of two terms
relating to the explanations provided by participants

Global versus Local refers to whether the pruning decision was primarily
influenced by ”Global” or ”Local” factors. “Global” factors refer to
horticultural concepts, such as those defined in 4.2.1, while “Local” factors
are tied to terms defined in the previous subsection, such as “Branch Vector”
or “Type of Most Common Bud”.

Pruning Context and Explanation: refers to the verbal description of the
context and/or explanation provided while pruning. These explanations can be
either Global or Local. An example of an explanation is “Growing into the
interior”. More explanations and contexts are reported in Section 4.2.1.

####  4.1.4 Physical Cuts

This category refers to the actual demonstrated cuts. This category is further
split into three terms that describe the cut itself (see Table 6).

Label |  Definition |  Example Label
---|---|---
Type of Cut |  The specific cut the pruner used on the branch |  Tipping, Thinning, Heading
Angle of Cut |  The physical angle of the lopper when cutting the branch relative to the main branch |  $45^{o}$, Parallel to main branch
Cut Placement |  Where the loppers were placed and the branch was cut (given in inches from base or tip of branch) |  3” from tip
Table 6: The Physical Cuts terminology category represents cut actions
performed by an individual during pruning. Our analysis revealed three labels:
Type of Cut, Angle of Cut, and Cut Placement.

Type of Cut refers to the specific cut the participant used on the branch and
is a general description of lopper placement on a branch (see Figure 10). We
identified three main cut types: “thinning”, “heading”, and “tipping”. These
cuts differ by where they are made on the branch. A “thinning cut” removes the
entire branch, with the loppers positioned any where from the base of the
branch to approximately one to two inches out. In contrast, a “tipping cut” is
placed at the end of the branch, and removes approximately two inches.
Finally, a “heading cut” is placed somewhere between the two, i.e., from two
to three inches from the base and the tip.

Figure 10: Observation of physical pruning cuts revealed three Type of Cuts:
thinning, heading, and tipping. These cuts vary in the location where the
tertiary branch is cut. For example, thinning cuts remove a branch at its
base, while tipping cuts only remove the last two inches.

Angle of Cut defines the physical angle of the lopper relative to the branch
being cut. These are given as both descriptive and numerical terms.
Descriptive includes relative placement, such as: “Parallel to the main
branch”. Numerical refers to the angle of the loppers relative to the
direction of the branch, the most common of which was “45 degrees”.

Cut Placement refers to where the branch was cut as approximately measured in
inches from either the base or the tip of the branch. This is related to the
Type of Cut term, but is an (approximate) numerical measurement instead of a
descriptive term.

###  4.2 Results: Discovered Horticultural Contexts and Pruning Heuristics

In this section we present the results of coding the horticultural contexts
and heuristics uncovered in the analysis. These results build on the pruning
terminology presented in the previous section. Our overall observation is that
the horticulturists begin with the horticultural context for a cut and use
that to describe why a cut was made, growers reference horticultural contexts
when defining their pruning heuristics, but primarily focus on the heuristics,
while the actual pruning is a visual and spatial task guided by those
heuristics.

We first present the horticultural contexts, along with the pruning heuristics
that were related primarily to each context. We then present examples from
actual pruning cuts that are representative of those heuristics, and which
visual/spatial characteristics map to those cuts for each tree architecture
and cultivars (UFO, Bing: Subsection 4.3; V-trellis, Envy: Subsection 4.4;
V-trellis, Jazz: Subsection 4.5). Finally, we discuss the types of visual-
spatial tasks pruners perform when pruning.

Figure 11: Summarized analysis technique mapping four terminology categories
to horticultural contexts and discovered pruning heuristics

####  4.2.1 Horticultural Contexts that Influence Pruning Decisions

Our grounded coding analysis of explanations provided during in-the-field
interviews and pruning observations revealed three common horticultural
motivations that influence pruning decisions (see Figure 12). These three
contexts are: environment management, crop-load management, and replacement
wood.

Figure 12: Pruning analysis revealed three main horticultural contexts that
influence pruning decisions: environmental management, crop-load management,
and replacement wood. Each context can map to at least one of the three cut
types, i.e., thinning, heading, and tipping, alongside not cutting.

####  4.2.2 Environmental Management

refers to pruning decisions that change the local environment. The primary
environmental concern is sunlight (removing branches that shade others), but
can also include air circulation (removing branches to improve circulation),
fungus or bacterial growth (removing diseased branches). For example, “…
thought process is managing crop load, but also managing light in the block to
thin it out, to allow for good channels of light from top to bottom”.
Specifically, every piece of fruit needs to have good light and air
circulation to be of high quality. An example of a pruning heuristic based on
this is removing more branches at the top of tree to allow for more light to
reach the fruit on the lower wires.

####  4.2.3 Crop-load Management

refers to controlling the amount of (potential) fruit on a branch. For
example, “…the balance of quantity and quality of fruit”. Pruning (removing
buds) is one of the methods used to control the quantity of fruit, which
indirectly influences the quality, since more buds leads to more, but smaller,
fruit [38, 22]. From a horticultural stand-point this is a complex calculation
that considers the plant growth characteristics of the cultivar, the two-year
growth cycle (buds for next year are set the previous year), and other effects
(pollination, fertilization, thinning). Typical pruning heuristics based on
this are 1) number of buds per branch and 2) even spacing of buds along the
branch.

####  4.2.4 Replacement Wood

refers to removing unproductive wood, which is characterized by wood with few
buds with respect to the branch’s length and diameter. Pruning for replacement
wood involves two steps: 1) determining which branch to remove to encourage
new growth, and 2) deciding which branch to leave to fill in space within the
tree’s architecture. Replacement wood pruning, therefore, considers current
and future tree growth. For example, pruners will leave one or two pieces of
lateral wood as possible secondary branch replacements to tie down on the tree
architecture’s wire system the following dormant pruning season after
completely removing the current secondary branch.

###  4.3 Upright Fruiting Offshoot Heuristics — Bing Cherries:

We report two heuristics for Bing Cherries growing with an Upright Fruiting
Offshoot architecture for environmental management and crop-load management.
These heuristics are derived 124 pruning cuts from five minutes of video data
from one horticulturist.

####  4.3.1 Environmental Management

Analysis of the estimated Branch Length for Bing Cherries revealed the average
estimated length of “lateral” branches pruned by the participant was 7.46
inches. Further investigation by Tertiary Branch Type showed the average
length for “Lateral Spur Wood” at 6.93 inches and 9.39 inches for ”Lateral
Vegetative Wood”. Out of the 124 cuts performed, we analyzed 106 branches
pruned were longer than six inches, while only 18 cuts, or 14.5% of all Bing
Cherry pruning cuts, violated this rule. Most length violations occurred in
“Lateral Spur Wood”, with 12 cuts, and the remaining six for “Lateral
Vegetative Wood”.

The horticulturist interview also explicitly identified branches with a
specific Branch Vector as a pruning heuristic for Bing Cherries. Specifically,
the horticulturist cited removing interior growth to prevent shading
(environmental management) of lower branches. Recall that Bing Cherries — due
to the Upright Fruiting Offshoot architecture — has four possible vectors (see
Figure 8). Of the 124 cuts categorized, 77 were “Parallel to the Wire”, 27
were “Into the Row”, 8 “Out of Row”, and 12 “Into the Interior”. All 12
branches growing “Into the Interior” were removed with a “Thinning Cut” at the
base of the branch and with an Angle of Cut “Parallel to the Main Branch.”
Similarly, our analysis of “Out of Row” branches to Type of Cut also saw all 8
branches removed with “Thinning Cuts” at the base of the branch with the same
lopper angle.

####  4.3.2 Crop-Load Management

Of the 64 “Lateral Vegetative Wood” classified, we coded 36 unique pruning
cuts (56.25%) with a Branch Vector “Parallel to the wire.” 21 of these had a
Branch Vigor considered “Weak” given shorter length to diameter ratio. For 16
of the 21 cuts that were “Weak, Lateral Vegetative Wood” growing “Parallel to
the Wire,” the participant utilized a “Thinning cut” while the remaining five
cuts were coded as “Tipping” (3) or “Heading” (2). All “Thinning cuts” had an
angle “Parallel to the Main Branch”. In contrast, the participant used a “45
degree” angle cut for all three tipping cuts and one of the two heading cuts.

When the participant used a tipping or heading cut they justified it with the
context of keeping or influencing future fruiting spur bud growth.
Specifically, the horticulturist said tipping “Lateral Vegetative Wood” would
result in the setting up fruiting spurs for following growing season. The
horticulturist called this tactic as being “greedy” given the expectation that
more fruiting spurs will set up for the next pruning season. Overall, tipping
should be used by the pruner when growers or pruners desire more fruit for the
next season, otherwise they should remove this “Weak, Lateral Vegetative Wood”
growing “Parallel to the Wire” with a thinning cut.

###  4.4 V-Trellis Heuristics — Envy Apples:

In this subsection, we report the heuristics for environmental management and
crop-load management for Envy Apples with a V-trellis architecture. We derive
these heuristics from quantitative analysis of 229 coded cuts from six minutes
of video and three participants (two pruners and one grower).

####  4.4.1 Environmental Management:

Similar to Bing Cherries, the horticulturists cited pruning back branches
growing “Into the Interior” for environmental management. From our analysis of
229 pruning cuts, we observed 36.24% of these cuts (83 cuts) were “Into the
Interior.” This Branch Vector was the most prominent of all five possible
branch vectors: “Into the Interior”, “Parallel to the Wire,” “Up from Wire,”
“Down from Wire,” and “Into the Row.” Of these 83 cuts, 73 were “Lateral Wood”
while the remaining 10 were “Spurs.” These 73 pruning cuts were distributed
across different wires in the Envy Apple architecture. Recall that the Envy
Apple tree utilized a 7-wire V-trellis architecture. Our pruning observations
focused on the bottom four wires with wire #1 being closest to the ground. For
the 73 cuts of “Lateral Wood” growing “Into the Interior” 10 were on wire #1,
6 on wire #2, 34 on wire # 3, and 20 on wire #4.

Figure 13: Analysis of the Type of Cut for “Lateral Wood” growing “Into the
Interior” compared to Observed Wire Number saw an inverse relationship between
“heading” and “thinning” cuts. Participants used “thinning cuts” on wires
higher from the ground, and “heading cuts” otherwise to allow for more light
to reach the lower wires.

Using this wire distribution, we analyzed what Type of Cut a participant used
when pruning was dependent on where in the tree they were pruning. The grower
explained during the in-field interview that there is a disparity in light
distribution throughout the tree with the tops of a tree receiving more light
than the bottom. As a result, pruners are more “aggressive” (i.e., remove more
branches) when pruning on higher wires to help create channels for sunlight to
reach lower branches. We observed an inverse linear relationship when
analyzing Type of Cut versus Observed Wire Number between the heading cuts and
thinning cuts as shown in Figure 13. The higher the wire from the ground, the
more a pruner used a “thinning cut,” while for wires closer to the ground, the
more a “heading cut” was used. For example, on wire #4, 65% of all the Type of
Cuts (13 of the 20 cuts) were “thinning cuts” compared to wire #1 with 40% (4
of the 10 cuts).

####  4.4.2 Crop-Load Management:

Apples, unlike Bing Cherries, need enough space between bud sites to allow for
fruit to grow to a desired quality. During the in-field interviews, the grower
described leaving enough space for an apple to grow. Likewise, the
horticulturist quantitatively defined leaving approximately four inches or a
“fist length” between each bud to produce the correct quality and quantity of
apple. Any buds closer than this are removed.

We coded 194 “Lateral Wood” tertiary branches. 59 or 30.41% of these branches
had a Branch Vector “Into the Row.” Of these branches, participants pruned 54%
of all “Into the Row” growth with a “heading cut” approximately four inches
from the base of the branch. The remaining 45.76% of all cuts were thinning
cuts. This fairly even distribution of “heading” and “thinning” cuts remained
when accommodating for the Branch Vigor. 31 “Into the Row” branches had
“Strong” vigor, and 28 had “Weak” vigor. Of the 28 “Weak” branches, there was
an even (50%) split between “heading” and “thinning” cuts with 14 branches for
the two Type of Cuts. Likewise, “heading” cuts were more prominent in “Strong”
vigor branches at 18 and 13 “thinning” cuts. Furthermore, all but one of the
32 “heading” cuts utilized an Angle of Cut of “45 degrees.” This Angle of Cut
was preferred by the grower as it controls the angle of the new growth.

By utilizing the “heading” cut compared to a “thinning” cut, we estimated 47
of the 197 buds (23.86%) remained after participants pruned back “Lateral
Wood” growing “Into the Row”. The total percentage of buds kept is greater
than the percentage of buds kept for all “Lateral Wood” at 18.28%, or 104 buds
out of 569. This cut behavior also decreased the average number of buds per
branch from three to one.

###  4.5 V-Trellis Heuristics — Jazz Apples:

In this subsection, we report three heuristics for Jazz Apples with a
V-trellis architecture for all three horticultural contexts. We labeled a
total of 308 from 2 minutes for four colored tags: 171 red, 129 green, 6
orange, and 2 white.

####  4.5.1 Environmental Management:

We extracted this heuristic from analysis of red tagged branches. The red tags
marked prunes to keep the fruiting structure within a desired area around the
secondary branch.

From the informal grower interview, the desired Cut Placement — represented by
tag placement — is anywhere between six and eight inches depending on where in
the tree (what wire) the branch is growing from. The grower explained that the
six inches was to stop the branch and apples from shading lower branches. We
coded 171 branches with a red tag. The Tertiary Branch Type of all 171
branches was “Lateral Wood,” with 113 (66.1%) having “Strong” Branch Vigor and
58 (33.9%) being “Weak”. Of these “Lateral Wood” branches, 28 had a Branch
Vector “Parallel to the Wire,” 54 were “Into the Row,” 47 were “Up from Wire,”
37 were “Into the Interior,” and 5 “Down from Wire.”

Using the red tag’s placement along the branch, approximately 81% of all Type
of Cuts tagged by the grower and expert pruner reflected a “heading cut,”
16.3% were “thinning cuts,” and 2.9% were “tipping cuts.” Of these heading
cuts, we see an average Cut Placement between four and six inches from the
branch base. However, there were instances on the first wire from the ground —
the grower referred to this as the bottom layer — when the tag placement was
approximately eight inches away from the branch’s base. The grower further
explained the longer cut placement away from the secondary branch is fine on
lower wires as there is less branches to block.

####  4.5.2 Crop-Load Management:

This heuristic is from our analysis of branches marked with a green tag. The
green tag indicated areas to prune for spacing between buds for fruit to grow.

Throughout our categorization of tagged branches from the informal grower
interview, we identified 129 green tags — 84 being “spur”, 45 being “Lateral
Wood” — containing approximately 280 buds. 160 of these buds belonged to
“Spurs” averaging around 2 buds a spur. These spurs were tagged by the grower
and expert pruner to remove with a “tipping” cut (64.29% or 54 cuts) compared
to “thinning” cuts (35.71% or 30 cuts) and no “heading” cuts. This
distribution of Type of Cut is consistent for “spurs” growing regardless of
Branch Vector, as shown in Figure 14.

Figure 14: The distribution of Type of Cut vs. Branch Vector for “spurs”
marked with a green tag in Jazz Apples. The “tipping” cut was utilized at
least half of all cuts regardless Branch Vector the “spur”. This prune allowed
the grower to prune for spacing and keep more buds this harvest year. The only
other cut marked were “thinning” cuts.

One reason pruners tip buds back is it allows growers to keep enough buds for
the current harvest year to produce fruit by leaving enough space for fruit to
grow. Like Envy apples, Jazz apples need space between buds to allow fruit to
grow to the desired quality. This spacing was identified by the grower during
the informal grower interview to be three inches apart.

By tagging “spurs” with a “tipping” cut, we estimated 53 of the 160 buds were
kept across the tagged tree instances we analyzed, or about 33.1% of all
“spur” buds. This is higher than the percentage of buds kept for all Tertiary
Branch Types at 28.21%, or 79 of the 280 buds.

####  4.5.3 Replacement Wood:

These branches for replacement wood removal were tagged with a white flag.
Furthermore, this heuristic was an explicitly stated rule by the grower during
the informal interview. Namely, any secondary branch that was larger than 1.2
inches in diameter is pruned for replacement. Specifically, the white flag
indicated the secondary branch was considered too vigorous. This vigor, as
described by the grower, affects first-year growth and are considered less
profitable due to producing less buds and having more erratic growth.

We coded two instances of branches tagged with a white flag. The placement of
the flag was placed as close as the base of the secondary branch to six inches
away. Furthermore, each white flag was also paired with an orange dot. Recall
the orange dot signifies an area (about 6 inches around the dot) that pruners
should not prune. This action allows for new branches to grow that can be the
“replacement,” or the next branch that will be wired down to fill space in the
V-trellis architecture.

###  4.6 Visual-Spatial Tasks Pruners Do

This subsection describes four visual-spatial decision tasks pruners perform
while pruning:

  1. 1.

Which bud to remove out of a bud cluster?

  2. 2.

Which branch do pruners leave for replacement?

  3. 3.

How far back to prune?

  4. 4.

How to identify extra vigor?

These visual-spatial tasks are the in-the-field (local) pruning decisions that
affect how a pruner prunes. Each of these factors can be affected by more than
one horticultural context defined in 4.2.1.

####  4.6.1 Which bud to remove out of a bud cluster?

In our example crop-load management heuristic for Jazz Apples, we describe
removing all but one bud in a bud cluster. This decision allows growers to
keep more buds while allowing each bud to have space for fruit to grow to
their desired size and quality. This requires the pruner to decide which of
the buds to remove and which to keep while pruning.

Currently, we do not know what factors influences a pruner’s decision to keep
certain buds. Some possible factors could be the direction and vigor of the
bud. Environmental management can also be a factor when considering how, if
the bud produced a fruit, would shade the tree. Future exploration with
pruning stakeholders is required to determine what influences this bud removal
decision.

####  4.6.2 Which branch do pruners leave for replacement?

Of the three horticultural contexts that influence pruning, one was referred
to as replacement wood. Replacement wood involves two parts: 1) completely
removing an unproductive branch, and 2) replacing the branch with new wood for
future growth. We identified with white flags in Jazz Apples when pruners
perform the first step in replacement wood when a branch reaches a diameter
greater than 1.2 inches. We also identified the area around the branch pruners
do not prune to produce replacement wood (orange dots). However, we did not
record any instances of branches that were used as replacement wood.

One influential factor in pruner’s selecting replacement wood is the ability
of the branch to be wired down. For example, in Envy and Jazz Apples,
replacement wood often replaces secondary branches along the wire system.
Branches that growth with a Branch Vector “Parallel to the Wire” would be
easier to tie down. Other factors such as Branch Length or Branch Vigor may
also influence this decision.

####  4.6.3 How far back to prune?

When pruning for sunlight management (light distribution) throughout a tree,
pruners need to consider how far back to prune branches. This is also referred
to pruning rigor by pruning stakeholders.

In our analysis we learned that pruning rigor is influenced by where in the
tree and how vigorous the branch. For example, “strong” vigor branches in Jazz
Apples should be pruned more rigorously compared to “weak” vigor so branches
to control Branch Vigor. Furthermore, the distance is also attributed to
experimenting with pruning rigor and how it affects crop-load management over
various seasons.

####  4.6.4 How to identify extra vigor?

Recall that branches exist on a Branch Vigor scale with one end being “weak”
and the other being “strong.” Pruners must be able to identify where along
this scale a branch fits when pruning. They also need to identify when a
branch is too vigorous. When branches become too vigorous, they impact fruit
quality by stealing more tree resources compared to other tertiary branches in
the tree and need to be completely removed.

In this paper we presented example branches with different Branch Vigor for
all three cultivars and the factors pruning stakeholders use to identify vigor
(see Figure 9). We do not have definitive values of when growers identify
branches as having too much vigor. Learning these values requires future
studies with pruning stakeholders.

###  4.7 Pruning Heuristics Discussion

In this section we presented the results of our grounded coding pruning
heuristic analysis across 13 minutes of video from two pruning protocols
(silent and speak-aloud) and tagged trees. We reported three contexts that
influence pruning decisions and report seven heuristics mapping between high-
level horticultural contexts to individual cuts for Bing Cherries, Envy
Apples, and Jazz Apples. More pruning contexts may exist that are not
presented in this paper. To learn these other contexts, this would require
further investigation and interviews with the three pruning stakeholders.

We also qualitatively introduced four visual-spatial tasks that affect how a
pruner prunes while in the field. These tasks were 1) which bud to remove in a
cluster, 2) which branch do pruners leave for replacement wood, 3) how far
back to prune, and 4) how to identify extra vigor. One area of future work is
performing studies to quantify these visual-spatial tasks. Researchers can
then implement these quantities into future autonomous pruning systems to more
accurately identify these tree features and improve the system’s pruning
decision making.

##  5 Discussion

In this work, we designed and implemented three studies with six pruning
stakeholders across three cultivars and two tree architectures. The first
study used a semi-structured interview format, based on a critical decision
analysis approach, to extract key pruning terminology and associated decision-
making variables grounded in actual tree feature exemplars (e.g., branch
geometry, buds). The second study represents a near-ideal version of the first
study where every cut on 28 trees was labeled with the reason for the cut.
Finally, we validated our pruning terminology and heuristics with a follow-up
validation study with two horticulturists.

Our grounded coding analysis allowed us to extract pruning terminology without
a priori assumptions (section 4.1). Although other architectures and cultivars
may have additional specific terms, the overall organization and terms of
interest are likely to be conserved. One key observation, however, is that the
terms need to be grounded in physical exemplars. In this study we relied on
our participants providing exemplars in the orchard, e.g., pointing at
branches that are “too vigorous” versus ones that are not. This relies on
there being nearby exemplars that span the range of vigor in order to
accurately capture what “vigor” means. In future work we propose using virtual
tree models (where we can continuously change, e.g., the branch length) to
more accurately capture how physical characteristics map to these qualitative
terms.

At a higher level, we extracted seven cultivar-specific pruning heuristics for
three horticultural contexts — environmental management, crop-load management,
and replacement wood (section 4.2). The horticultural contexts are based in
plant biology and are likely to be similar across all perennial crops.
Moreover, they are also (relatively) easy to estimate from the physical plant
geometry (e.g., modeling light transport ( Boudon et al [5]). Although the
pruning heuristics are very cultivar-dependent, their overall structure
(context plus physical features) is similar. These heuristics are also more
amenable to automation, providing both a high-level goal (the context) plus
what physical features (e.g., branch length, bud spacing) are used to make
that decision. Again, the challenge is linking specific qualitative statements
(“too far into the row”) to numerical quantities.

Numerous studies (and the growers themselves) have shown that the specific set
of cuts a pruner chooses can vary widely, even on the same tree and even with
the same pruner. While some cuts are unambiguous (e.g., removing excessively
vigorous growth that shades lower branches) the majority of cuts are
optimizing a function that has multiple solutions. If a branch has twelve
buds, and should have at most ten, that is 12 choose 2 equals 66 combinations.
While secondary considerations (bud spacing, chose buds on less vigorous
branches) may reduce that number down, it is not surprising that you may see
three or four different solutions. It is not clear if it is possible (or
desirable) to capture this fine-grained decision making for purposes of
automation.

We next provide observations and recommendations for future researchers
interested in capturing pruning heuristics for autonomous pruning systems.

Going into the field is irreplaceable. For determining what pruning heuristics
exist based on a particular tree cultivar’s architecture, we needed to see
example prunes in the field alongside the verbal explanations. This field
interaction allowed our team to ask follow-up and what-if questions and for
the interviewee to point to exemplars and visually demonstrate concepts. It
also allowed us to capture a visual data set of various tree features that can
be used in future autonomous vision systems.

Interacting with all pruning stakeholders is beneficial. Through our study we
identified three major stakeholders: horticulturists, growers, and pruners.
Each stakeholder held different knowledge. If we had interacted with only the
horticulturists we would have only uncovered the horticultural reasons for
pruning, and not how those reasons were (on a practical level) implemented by
the pruners as a visual-spatial task.

Putting numbers to terms and visual characteristics. Bridging the gap between
qualitative statements (“too vigorous”) and measurable quantities (number and
spacing of buds, thickness and length of branch, texture and color of bark) is
an open challenge. Being in the field and observing exemplars is a necessary
first-step, but correctly mapping measurable quantities to these qualitative
terms is likely to require a structured study for any complex quantity.

How good should sensors be for successful pruning autonomous systems? In order
to implement the heuristics reported in this paper into physical autonomous
pruning systems requires sensors. These sensors can measure how thick and long
branch are and the spacing between buds. What is currently unknown is how
accurate should these sensors be? Moreover, how much error can these sensors
have that does not impact the robot’s ability to make pruning decisions and
perform cuts?

##  6 Conclusion

This paper presents the design and implementation of three studies across
three tree cultivars — Bing Cherries, Envy Apples, and Jazz Apples — and two
tree architectures — Upright Fruiting Offshoot and V-Trellis. Our three
studies were 1) a semi-structured interview with two pruning protocols (silent
and speak-aloud), 2) an informal interview with tagged trees, and 3) an online
validation interview. We analyzed six hours of video data of pruning
observations and transcripts from six pruning stakeholders categorizing 661
distinct physical pruning cuts and tagged tree instances using a modified
grounded coding analysis technique. This technique allowed us to discover a
list of pruning terminology for communicating with stakeholders about pruning
heuristics and a set of seven heuristics that connect global horticultural
contexts to localized pruning decisions. This terminology and heuristics can
be implemented into future autonomous pruning systems. We recommend that
future researchers interested in autonomous pruning go out in the field and
communicate with all three pruning stakeholders — horticulturists, growers,
and pruners — to capture relevant pruning concepts and more holistic
understanding of the decisions that influence pruning.

## Statements and Declarations

This material is based upon work supported by the AI Research Institutes
program supported by NSF and USDA-NIFA under the AI Institute: Agricultural AI
for Transforming Workforce and Decision Support (AgAID) award No,
2021-67021-35344

The first author is supported by the Achievement Rewards for College
Scientists (ARCS) Foundation Scholar Oregon Chapter.

There are no other conflicts of interest.

Acknowledgements This material is based upon work supported by the AI Research
Institutes program supported by NSF and USDA-NIFA under the AI Institute:
Agricultural AI for Transforming Workforce and Decision Support (AgAID) award
No, 2021-67021-35344

## References

  *   * Ag America Lending [2022] Ag America Lending (2022) (infographic) the u.s. labor shortage
  * Akbar et al [2016] Akbar SA, Elfiky NM, Kak A (2016) A novel framework for modeling dormant apple trees using single depth image for robotic pruning application. In: 2016 IEEE international conference on robotics and automation (ICRA), IEEE, pp 5136–5142
  * Alrigiwirsah et al [2020] Alrigiwirsah A, Lubis RM, Novita A (2020) The effect of pruning and chicken manure on vegetative growth of honey deli (syzygiumaqueum burn f.) in 9 months age. In: Proceeding International Conference Sustainable Agriculture and Natural Resources Management (ICoSAaNRM)
  * Botterill et al [2017] Botterill T, Paulin S, Green R, et al (2017) A robot system for pruning grape vines. Journal of Field Robotics 34(6):1100–1122
  * Boudon et al [2012] Boudon F, Pradal C, Cokelaer T, et al (2012) L-py: an l-system simulation framework for modeling plant architecture development based on a dynamic language. Frontiers in plant science 3:76
  * Bryant and Charmaz [2007] Bryant A, Charmaz K (2007) The Sage handbook of grounded theory. Sage
  * Carroll [2017] Carroll B (2017) Annual pruning of fruit trees
  * Charmaz [2014] Charmaz K (2014) Constructing grounded theory, 2nd edn. sage
  * Chun and Knight [2020] Chun B, Knight H (2020) The robot makers: An ethnography of anthropomorphism at a robotics company. ACM Transactions on Human-Robot Interaction (THRI) 9(3)
  * Corbett-Davies et al [2012] Corbett-Davies S, Botterill T, Green R, et al (2012) An expert system for automatically pruning vines. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand. Association for Computing Machinery, New York, NY, USA, IVCNZ ’12, p 55–60, 10.1145/2425836.2425849
  * Daniels [2018] Daniels J (2018) From strawberries to apples, a wave of agriculture robotics may ease the farm labor crunch
  * Du et al [2011] Du X, Chen D, Zhang Q, et al (2011) Mechanical harvesting of ufo cherry: Investigation of tree plant dynamics. In: 2011 Louisville, Kentucky, August 7-10, 2011, American Society of Agricultural and Biological Engineers, p 1
  * Economy [2019 [Online]] Economy NA (2019 [Online]) Immigrants and american agriculture
  * Fallatah et al [2020] Fallatah A, Chun B, Balali S, et al (2020) ’would you please buy me a coffee?’ how microcultures impact people’s helpful actions toward robots. In: Proceedings of the 2020 ACM Designing Interactive Systems Conference
  * Gallardo et al [2012] Gallardo RK, Taylor MR, Hinman HR (2012) 2009 cost estimates of establishing and producing Gala apples in Washington. Washington State University Extension
  * He and Schupp [2018] He L, Schupp J (2018) Sensing and automation in pruning of apple trees: A review. Agronomy 8(10):211
  * Henwood and Pidgeon [2003] Henwood K, Pidgeon N (2003) Grounded theory in psychological research.
  * Kaba and Abunyewa [2021] Kaba JS, Abunyewa AA (2021) New aboveground biomass and nitrogen yield in different ages of gliricidia (gliricidia sepium jacq.) trees under different pruning intensities in moist semi-deciduous forest zone of ghana. Agroforestry Systems 95(5):835–842
  * Karkee et al [2014] Karkee M, Adhikari B, Amatya S, et al (2014) Identification of pruning branches in tall spindle apple trees for automated pruning. Computers and Electronics in Agriculture 103:127–135
  * Klein et al [1989] Klein GA, Calderwood R, Macgregor D (1989) Critical decision method for eliciting knowledge. IEEE Transactions on systems, man, and cybernetics 19(3):462–472
  * Kolmanič et al [2021] Kolmanič S, Strnad D, Kohek Š, et al (2021) An algorithm for automatic dormant tree pruning. Applied Soft Computing 99:106931
  * Kon and Schupp [2018] Kon TM, Schupp JR (2018) Apple crop load management with special focus on early thinning strategies: A usa perspective. Hort Rev 46:255–298
  * Kupka [2007] Kupka I (2007) Growth reaction of young wild cherry (prunus avium l.) trees to pruning. Journal of Forest Science 53(12):555–560
  * Lang et al [2015] Lang GA, Musacchi S, Whiting MD, et al (2015) Cherry training systems
  * Maughan et al [2017] Maughan T, Black B, Roper T (2017) Training and pruning apple trees. Accessed November 20, 2022 [Online]
  * Mohajan and Mohajan [2022] Mohajan D, Mohajan H (2022) Development of grounded theory in social sciences: A qualitative approach
  * Pandit [1996] Pandit NR (1996) The creation of theory: A recent application of the grounded theory method. The qualitative report 2(4):1–15
  * Persello et al [2019] Persello S, Grechi I, Boudon F, et al (2019) Nature abhors a vacuum: Deciphering the vegetative reaction of the mango tree to pruning. European Journal of Agronomy 104:85–96
  * Polomski [2019] Polomski RF (2019) Pruning & training apple & pear trees. Accessed November 20, 2022 [Online]
  * Poni et al [2016] Poni S, Tombesi S, Palliotti A, et al (2016) Mechanical winter pruning of grapevine: Physiological bases and applications. Scientia Horticulturae 204:88–98
  * Robinson et al [2008] Robinson T, Hoying S, Reginato G (2008) The tall spindle planting system: Principles and performance. In: IX International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems 903, pp 571–579
  * Robinson et al [2006] Robinson TL, Hoying SA, Reginato GH (2006) The tall spindle apple production system. New York fruit quarterly 14(2):21–28
  * Rothwell et al [2022] Rothwell M, Stone J, Davids K (2022) Investigating the athlete-environment relationship in a form of life: An ethnographic study. Sport, Education and Society 27(1)
  * Schupp et al [2017a] Schupp JR, Winzeler HE, Kon TM, et al (2017a) A method for quantifying whole-tree pruning severity in mature tall spindle apple plantings. HortScience 52(9):1233–1240
  * Schupp et al [2017b] Schupp JR, Winzeler HE, Kon TM, et al (2017b) A method for quantifying whole-tree pruning severity in mature tall spindle apple plantings. HortScience horts 52(9):1233 – 1240. 10.21273/HORTSCI12158-17
  * Sevila [1985] Sevila F (1985) A robot to prune the grapevine. In: Agri-Mation 1, Chicago, Ill.(USA), 25-28 Feb 1985, ASAE
  * Silverman et al [2021] Silverman GS, Baroiller A, Hemer SR (2021) Culture and grief: Ethnographic perspectives on ritual, relationships and remembering
  * Verma et al [2022] Verma P, Sharma S, Sharma N, et al (2022) Review on crop load management in apple (malus x domestica borkh.). The Journal of Horticultural Science and Biotechnology pp 1–23
  * Von Feigenblatt and Acuña [2021] Von Feigenblatt O, Acuña B (2021) Two case studies dealing with social studies at the secondary level: Applied anthropology and grounded theory. Journal of Alternative Perspectives in the Social Sciences 11(2):237–252
  * Westling et al [2021] Westling F, Underwood J, Bryson M (2021) A procedure for automated tree pruning suggestion using lidar scans of fruit trees. Computers and Electronics in Agriculture 187:106274. https://doi.org/10.1016/j.compag.2021.106274
  * Whiting et al [2018] Whiting M, et al (2018) Precision orchard systems. Automation in tree fruit production: Principles and practice pp 75–93
  * Whiting [2011] Whiting MD (2011) The ufo architecture: A novel system for high efficiency sweet cherry orchards. In: HORTSCIENCE, AMER SOC HORTICULTURAL SCIENCE 113 S WEST ST, STE 200, ALEXANDRIA, VA 22314 …, pp S124–S124
  * You et al [2022] You A, Grimm C, Silwal A, et al (2022) Semantics-guided skeletonization of upright fruiting offshoot trees for robotic pruning. Computers and Electronics in Agriculture 192:106622
  * Zhang et al [2022] Zhang D, Cai W, Zhang X, et al (2022) Different pruning level effects on flowering period and chlorophyll fluorescence parameters of loropetalum chinense var. rubrum. PeerJ 10:e13406