# CitDet: A Benchmark Dataset for Citrus Fruit Detection

Jordan A. James1∗, Heather K. Manching2∗, Matthew R. Mattia3, Kim D. Bowman3,
Amanda M. Hulse-Kemp2,4, and William J. Beksi1 ∗ Indicates equal
contribution.1The authors are with the Department of Computer Science and
Engineering, The University of Texas at Arlington, Arlington, TX, USA. Emails:
<EMAIL_ADDRESS><EMAIL_ADDRESS>2The author is with the
Department of Crop and Soil Sciences, North Carolina State University,
Raleigh, NC, USA. Email<EMAIL_ADDRESS>3The authors are with the
Subtropical Insects and Horticulture Research Unit, USDA Agricultural Research
Service, Ft. Pierce, FL, USA. Emails<EMAIL_ADDRESS><EMAIL_ADDRESS>4The author is with the Genomics and Bioinformatics
Research Unit, USDA Agricultural Research Service, Raleigh, NC, USA. Email:
<EMAIL_ADDRESS>
###### Abstract

In this letter, we present a new dataset to advance the state of the art in
detecting citrus fruit and accurately estimate yield on trees affected by the
Huanglongbing (HLB) disease in orchard environments via imaging. Despite the
fact that significant progress has been made in solving the fruit detection
problem, the lack of publicly available datasets has complicated direct
comparison of results. For instance, citrus detection has long been of
interest in the agricultural research community, yet there is an absence of
work, particularly involving public datasets of citrus affected by HLB. To
address this issue, we enhance state-of-the-art object detection methods for
use in typical orchard settings. Concretely, we provide high-resolution images
of citrus trees located in an area known to be highly affected by HLB, along
with high-quality bounding box annotations of citrus fruit. Fruit on both the
trees and the ground are labeled to allow for identification of fruit
location, which contributes to advancements in yield estimation and potential
measure of HLB impact via fruit drop. The dataset consists of over 32,000
bounding box annotations for fruit instances contained in 579 high-resolution
images. In summary, our contributions are the following: (i) we introduce a
novel dataset along with baseline performance benchmarks on multiple
contemporary object detection algorithms, (ii) we show the ability to
accurately capture fruit location on tree or on ground, and finally (ii) we
present a correlation of our results with yield estimations.

###### Index Terms:

Agricultural Automation; Object Detection, Segmentation and Categorization;
Robotics and Automation in Agriculture and Forestry

## Multimedia Material

The dataset, software, and documentation for fruit detection and counting can
be found at [1].

##  I Introduction

Figure 1: The CitDet dataset contains precise bounding box object annotations
for fruit on tree and fruit on ground (top row). It also has images from
multiple different tree rows including a large variety of citrus (bottom row).

Fruit detection and counting in orchards are crucial tasks for agricultural
automation. They can be used to reduce routine farming and breeding activities
as well as provide insightful estimates for harvest and forthcoming growing
seasons. Moreover, accurate fruit detection enables the possibility of robotic
harvesting, which has the potential to eliminate one of the most labor-
intensive processes for growers.

Many imaging and sensing technologies have been used for detecting fruit such
as hyperspectral [2], laser scanning [3], thermal [4], and red-green-blue
depth (RGBD) sensors [5, 6], yet the most common technology is the standard
RGB camera. Although conventional RGB cameras are widely accessible, they
present several challenges for in-orchard fruit detection such as variation in
appearance, irregular lighting, and severe occlusion. Recent works have used
deep learning to overcome these challenges [7]. However, due to the lack of
standardized benchmark datasets for agricultural automation, it is difficult
to compare existing methods with each other. To tackle this problem we
establish a new benchmark dataset, CitDet, for citrus fruit detection and
counting in orchard settings together with a comprehensive analysis of state-
of-the-art object detection algorithms, Fig. 1.

Computer vision techniques combined with deep learning are appealing in
agricultural automation due to their powerful prediction capabilities and non-
invasive nature. Nonetheless, such methods require huge amounts of data to
perform with high accuracy. While there exists large datasets (e.g., COCO [8])
that have allowed for the development of new algorithms, many automation tasks
require custom datasets to achieve meaningful results. For example, although
COCO contains a class for oranges an orange detector trained solely from the
dataset instances will perform poorly in an orchard setting. This is due to
the complex background scenes in COCO, which require the detector to have more
background classification mechanisms resulting in less orange classification
mechanisms [9]. Another reason for the weak performance stems from many
instances of oranges with very little or no occlusions in COCO. In orchard
fields, oranges will be occluded by other instances of oranges as well as the
surrounding foliage. Furthermore, orange instances will not be a large
percentage of the image when an entire tree is imaged. These pitfalls
highlight the need for unique datasets to carry out fruit detection, yield
mapping, and much more.

###  I-A Citrus Greening

Citrus greening, also known as Huanglongbing (HLB), is a serious bacterial
disease that affects citrus trees and is threatening the existence of the
citrus industry, particulary in the southeastern United States [10]. HLB is
transmitted by the Asian citrus psyllid, a small insect that feeds on the
leaves and shoots of citrus trees. Symptoms of citrus greening include
yellowing of the leaves, uneven ripening of the fruit, and reduced fruit size
and yield, both in production of fruit as well as premature fruit loss with
fruits dropping to the ground. If left untreated, the disease will ultimately
lead to the death of the tree. Currently, there are no effective treatments to
cure trees affected by citrus greening. The best way to control the spread of
the disease is through a combination of methods such as cultural, biological,
chemical, and genetic control along with nutritional management. Nevertheless,
none of these countermeasures are foolproof. HLB continues to spread and cause
major economic losses for citrus growers.

###  I-B Challenges in Detecting Citrus Fruits in Native Orchard Environments

Although public datasets for citrus detection exist (e.g., OrangeSORT [11]) to
the best of our knowledge, CitDet is the first dataset to include images of
citrus affected by HLB. An orange detector trained on existing datasets would
perform poorly on fruit from an infected tree as the fruits are likely to be
discolored and deformed. Images in contemporary datasets are also low
resolution, thus limiting the amount of information to learn and predict from.
Finally, the aforementioned datasets only annotate fruit on trees. This can be
an issue when training a detector for yield estimation since the model will
not learn the difference between dropped fruit and fruit still on the tree,
which can be helpful in identifying symptoms of HLB or secondary fungus
associated with post bloom fruit drop. In contrast, CitDet aims to enhance
automation for citrus orchards through high-resolution images of HLB infected
citrus trees with accurate annotations for both fruit on tree and fruit on
ground. Our contributions are as follows.

  * •

We captured 579 high-resolution images of citrus trees affected by HLB.

  * •

We provide over 32,000 high-quality annotations of in-orchard citrus, with
separate classes of fruit, both on the ground and on the trees.

  * •

We analyze the baseline performance of detecting citrus fruits using multiple
state-of-the-art object detection algorithms.

The remainder of this paper is organized as follows. Relevant related
literature is reviewed in Section II. We discuss the image collection and
annotation procedures in Section III and Section IV, respectively. Dataset
statistics are outlined in Section V and an evaluation of the benchmark
algorithms is presented in Section VI. In Section VII, we conclude and mention
future work.

##  II Related Work

Benchmark datasets provide the means to train and evaluate new algorithms,
they are essential to the advancement of deep learning methods. Datasets such
as ImageNet [12], Pascal VOC [13], and COCO [8], have publicly released
millions of labeled images that contain a variety of classes and objects. Even
though these datasets have enabled breakthroughs in image classification,
object detection, and object segmentation, there is still a demand for
specialized datasets in agricultural automation. While the number of datasets
for agricultural automation continues to grow, there still remains a lack of
data on fruit affected by common diseases.

###  II-A Fruit Detection

In-orchard detection of fruits is vital for agricultural tasks including fruit
yield estimation and automated picking. Classical methods for fruit detection
involve the use of static color thresholds. These methods were enhanced by
adding additional sensors such as thermal and near-infrared cameras [14]. In
recent years, deep learning-based approaches have shown great promise in the
domain of fruit detection. For example, Bargoti and Underwood [15] and Häni et
al. [16] used region-based convolutional neural networks (RCNNs) to detect
fruits in images. Liu et al. [17] utilized a fully-convolutional neural
network to segment and detect fruits in a tracking pipeline. Many current
works have used the YOLO [18] family of object detectors for fruit tracking
and yield estimation (e.g., [19, 20, 11]).

TABLE I: Comparison of fruit detection datasets Fruit Detection | Fruit | # Images | # Annotations | # Classes | Resolution | Ground Truth | Public
---|---|---|---|---|---|---|---
Bargoti et al. [15] | Apple | 841 | 5765 | 1 | 308 x 202 | Circles | Yes
Stein et al. [21] | Mango | 1404 | 7065 | 1 | 500 x 500 | Circles | Yes
Zhang et al. [11] | Citrus | - | - | 1 | 800 x 1240 | Boxes | Yes
Häni et al. [16] | Apple | 1001 | 41,325 | 1 | 1280 x 720 | Polygons | Yes
Hou et al. [20] | Citrus | 4,855 | 17,567 | 1 | 1280 x 720 | Boxes | Yes
CitDet (ours) | Citrus | 579 | 32,502 | 2 | 2448 x 3264 | Boxes | Yes

###  II-B Comparison of Datasets

Table I highlights the issues with current fruit detection datasets. Due to
the high cost and time involved in labeling data, researchers have opted to
use small datasets resulting in a lack of variety. Many works attempt to
remedy this by tiling images into smaller sections to artificially increase
the overall number of images [15]. Although this technique expands the dataset
size, it only shows a small part of the original image and it does not change
the total number of object instances. Furthermore, the diversity of the
dataset does not increase, which can lead to overfitting when developing
machine learning models. While recent research has made available many labeled
citrus images [20], the images fail to capture the entire tree and they can
suffer from similar overfitting problems.

Another issue is that many of the datasets, for example the MinneApple [16]
dataset, contain fruit on the ground without any labels. This can lead to
ambiguity when training a model as it must learn to classify fruit on the
ground as “no object” even though the features are similar to the features of
fruit on the tree. Our dataset aims to fix these issues and provide
researchers with a means to evaluate and compare their algorithms. To the best
of our knowledge, CitDet is the first fruit detection dataset to provide full-
resolution images with annotations for both fruit on trees and on the ground.
It is the only citrus dataset to contain images of entire citrus trees. In
addition, we include a variety of citrus tree species imaged at different
stages of maturity under varying illumination conditions to avoid overfitting.

##  III Image Collection

CitDet is composed of images captured at the USDA Agricultural Research
Service Subtropical Insects and Horticulture Research Unit in Ft. Pierce,
Florida, between October 2021 and October 2022. The orchard contains a large
assortment of citrus tree species and is used for genomics and phenotyping
research involving citrus infected with HLB. We collected 579 images from
different sections of the orchard using Field Book [22] on Android tablets.
While imaging the plants, we faced the camera in a portrait orientation
directly centered on the tree of interest. All images were taken at the edge
of the soil in the tree row to simulate a ground-based robot imaging the tree
while moving between two rows of trees.

Data was collected over the course of one year to allow the fruit to be imaged
at different stages of the ripening cycle during citrus production from
October to March. Due to the nature of HLB and the mixture of fruit maturity,
CitDet contains fruit of different colors and sizes. All trees were imaged
from both the sunny and shady sides of the tree row and imaging was done over
multiple days to account for variations in weather and lighting conditions.
The imaged trees consist of over 50 different varieties of citrus. The dataset
was split into 80% for the training set and 20% for the test set.

Images included in the test set were chosen to cover a variety of citrus tree
species and cultivars with fruits of varied color, shape, and size.
Additionally, we collected observed yield estimates for 187 randomly selected
trees by counting the number of fruit per tree prior to imaging. Out of the
acquired images, 130 were not included in the final dataset. Since we do not
include a benchmark or dataset for tracking, we use the predicted detections
from the model as yield estimation and validate against the ground truth.

##  IV Image Annotation

To construct CitDet, we followed standard annotation methods found in other
benchmark object detection datasets. The Roboflow [23] annotation tool was
used to label the image data with bounding boxes. Specifically, we labeled all
visible fruit in the foreground regardless of occlusion, while fruit in the
background was only labeled for instances with no occlusion. We do not provide
any annotations for fruits that are fully occluded and thus not visible. Each
of the labeled objects in an image are classified as either fruit on tree or
fruit on ground.

CitDet is unique for fruit detection in that we provide labels for fruit that
has already fallen. This allows detection methods to accurately identify fruit
that contributes to the overall fruit yield. Since a single image can contain
a large number of object instances and heavy amounts of occlusion, labeling
images is a time-consuming task. For instance, annotating a single image can
take up to half an hour. Therefore, the task of instance labeling was split
among several internal volunteers and each image was assigned to only one
volunteer for annotation. After labeling, each image was then reviewed by a
single expert to validate the quality of the annotations.

##  V Dataset Statistics

Popular image datasets (e.g., ImageNet [12], PASCAL VOC [13], and COCO [8])
focus on detecting many categories of objects in a multitude of natural
settings. In each of these datasets, most object instances tend to occupy over
10% of the image area. As a result, the majority of images contain few object
instances per image. In comparison, CitDet provides a unique collection of
small, highly-cluttered objects under various levels of occlusion.

Since the CitDet dataset does not contain many different categories, it is
comparable to the Caltech Pedestrian Detection [24], KITTI [25], and
MinneApple [16] datasets where each dataset focuses on providing many labeled
object instances per category. Specifically, our dataset is similar to
MinneApple as both provide a large number of annotations for fruit in an
orchard setting. The main difference is that our dataset provides annotations
of fallen fruit as well as fruit still on the tree. CitDet allows models to
automatically distinguish between dropped and non-dropped fruit, enabling
accurate yield estimation without any additional postprocessing.

Figure 2: The area distribution of the objects in CitDet. The dataset is
mainly comprised of small object instances with an area of less than 502
pixels.

CitDet also provides a challenge for state-of-the-art detection algorithms due
to the small average size of the object instances. Small objects are generally
harder to detect and require specialized network structures. In COCO,
ImageNet, and PASCAL VOC, only 50% of the objects occupy less than 10% of an
image while the remaining 50% of the objects occupy between 10% and 100% of an
image. Conversely, over 90% of the objects in CitDet take up less than 10% of
an image. The average object in our dataset is only $50\times 50$ pixels (Fig.
2), which corresponds to less than 0.1% of an image. In contrast, objects in
MinneApple occupy around 0.17% of an image. Another key difference between
CitDet and MinneApple is the format of the object annotations, bounding boxes
versus polygons, respectively. We provide annotations in bounding box format
allowing for maskless frameworks to be easily applied. This eliminates the
need to infer bounding boxes from masks, which can induce errors due to the
highly cluttered and occluded nature of in-orchard fruit.

##  VI Algorithmic Analysis

We applied multiple state-of-the-art object detection algorithms on CitDet to
establish a baseline for future work. For all experiments, we used the same
460 images from our dataset for training while the remaining 119 images were
utilized as the test set. We divided the experiments into two tasks: (i)
detection on the whole (full-resolution) image dataset, and (ii) detection on
the tiled image dataset. The tiled image dataset was created by splitting
every image into 9 separate images using a $3\times 3$ cropping method. For
any bounding box split by the cropping, a bounding box for each portion of the
object was created in each of the new images resulting in slightly more object
instances.

###  VI-A Fruit Detection

In all experiments, the models were trained using an NVIDIA Tesla T4 GPU and
restricted to similar levels of compute power. Every algorithm tested used
pretrained COCO weights for initialization and they were fine-tuned for 50
epochs on the tiled dataset. The models tested on the whole image dataset used
pretrained weights from the tiled dataset and were fine-tuned for 50 epochs on
resized images of $704\times 704$ resolution. We evaluated each of the
algorithms on the test set and reported the average performance for each class
as well as the mean average performance of all classes.

Detection evaluation metrics: We followed the established evaluation protocols
used by other benchmark object detection datasets (e.g., [13, 8, 16]). For a
single class, we reported the average precision (AP) as the main evaluation
metric and for all classes we noted the mean AP (mAP) of all classes. For the
AP of a class we used an intersection over union (IoU) threshold of 0.5,
increased it in intervals of 0.05 up to 0.95, and reported the mean of the AP
at each threshold. The AP at thresholds of 0.5 and 0.75, as well as the AP for
small (APS), medium (APM), and large (APL) objects, are also provided.

FasterRCNN: FasterRCNN [26] utilizes a ResNet-50 [27] backbone along with a
feature pyramid network. The implementation makes use of region proposal
networks as the head of the detection network and two branches for predicting
the class and bounding boxes. We used a k-means clustering algorithm with IoU
and ground-truth labels in the training dataset to determine the anchor box
sizes. All the other optimization parameters were the same as the original
paper.

(a) FasterRCNN

(b) YOLOv5

(c) YOLOv7

Figure 3: Visualizations of qualitative results for each network on the same
full validation image. Bounding box annotations are colored based on the
predicted class label with differing color schemes for each network.

(a) YOLOS

(b) DETR

(c) FasterRCNN

(d) YOLOv5

(e) YOLOv7

Figure 4: Visualizations of qualitative results for each network on the same
tiled validation image. Bounding box annotations are colored based on the
predicted class label with differing color schemes for each network.

YOLOv5: The implementation of YOLOv5 is provided by Ultralytics [28]. YOLOv5
uses a different architecture compared to previous versions of YOLO [18]. In
particular, it has a cross stage partial backbone architecture, which is a
combination of two types of convolutional layers: depth-wise convolution and
point-wise convolution. The architecture not only reduces the computational
and memory requirements of the model, but also improves the overall accuracy.

YOLOv7: YOLOv7 [29] improves upon real-time object detection performance by
designing a trainable “bag-of-freebies” methods. The detector addresses two
new issues in object detection evolution by proposing the “extend” and
“compound scaling” techniques. The proposed methods reduce the number of
parameters and amount of computation by approximately 40% and 50%,
respectively. They also result in faster inference speed and higher detection
accuracy.

DETR: The DETR [30] model for object detection was introduced by Facebook
Artificial Intelligence Research. It uses a ResNet-50 backbone to embed image
features as input to a transformer encoder-decoder. The architecture utilizes
a standard transformer encoder-decoder with the addition of object query
tokens passed to the decoder. The network is trained using a bipartite
matching loss thus removing the need for non-maximum suppression. We used sine
positional embeddings for both the encoder and decoder.

YOLOS: You Only Look at One Sequence (YOLOS) [31] leverages a vanilla vision
transformer (ViT) [32] encoder with minimal multilayer perceptron heads for
object detection. The YOLOS ViT architecture is the same as DeiT [33] and
includes a distillation token for training. Images are represented by a
sequence of $16\times 16$ patches. The patches are projected and inputted as
tokens to the transformer encoder along with positional embedding tokens.
YOLOS feeds detection token inputs to an encoder, which are then decoded by
the head to predict the box and class. Similar to DETR, the network is trained
using a bipartite matching loss. We utilized sine positional embeddings for
the implementation tested.

###  VI-B Discussion

Contrary to other fruit detection works, we found that processing the image in
a tiled manner performs better than detectors that operate on the full image.
We hypothesize that there are two primary factors for this. The first factor
is the difference between the tiling methods used. For example, in the Bargoti
and Underwoods [15] model, training images are split into overlapping chunks,
which requires filtering of overlapping bounding boxes. Our tiling method does
not have any overlap. Instead, we process the bounding boxes on the edges of
the fruits before training and therefore do not require the filtering step at
the end. The second factor contributing to the improved performance of the
tiled versions is the ability to preserve information when resizing the image
for the model.

TABLE II: Whole Image Dataset Results Model | #params | AP | AP50 | AP75 | APS | APM | APL
---|---|---|---|---|---|---|---
FasterRCNN | 41.3M | 22.0 | 51.5 | 14.0 | 21.8 | 44.2 | -
YOLOv5 | 21.1M | 34.8 | 70.0 | 29.8 | 9.0 | 36.9 | 46.3
YOLOv7 | 36.5M | 40.6 | 77.9 | 36.7 | 12.3 | 42.6 | 49.7
TABLE III: Tiled Image Dataset Results Model | #params | AP | AP50 | AP75 | APS | APM | APL
---|---|---|---|---|---|---|---
YOLOS | 30.9M | 32.4 | 70.7 | 24.6 | 10.3 | 35.8 | 47.9
DETR | 41.3M | 35.0 | 72.8 | 28.8 | 9.8 | 38.7 | 49.8
FasterRCNN | 41.3M | 37.2 | 76.0 | 31.5 | 24.1 | 44.4 | 45.9
YOLOv5 | 21.1M | 44.9 | 81.9 | 43.4 | 18.2 | 49.2 | 50.6
YOLOv7 | 36.5M | 45.5 | 83.1 | 43.9 | 19.2 | 49.4 | 52.1

In comparing the state-of-the-art object detection algorithms, we found that
YOLOv7 outperforms all the other algorithms on both the whole and tiled
images. However, FasterRCNN performs the best for small objects on both image
types. The results in Tables II and III suggest that the YOLO architectures
benefit more from transfer learning on high-resolution images when compared to
FasterRCNN. All the networks demonstrate the capability to distinguish between
fruit yield and drop even in ambiguous cases such as in Fig. 3 and 4. We
observed that all the methods struggled to accurately detect small object
instances. We note that standard convolutional approaches performed better
than transformers for small object detection. Nonetheless, future research is
needed to accurately detect small and medium sized objects for all methods.
Our findings confirm previous works (e.g., [34, 16]), which found that object
size is one of the main determinants of error in object detection.

###  VI-C Yield Estimation

To further validate the detection results, we used a detection only based
yield estimation method for each tree and compared it with the ground-truth
estimates as currently used in breeding programs for observed fruit yield
values. In this experiment, we acquired front and back images of 187 trees
before harvest. A YOLOv7 model was trained on a subset of CitDet which
excluded images of trees in the yield estimation test set. Breeding program
measured fruit yield estimates for each tree were recorded and used as the
ground-truth yield values. In all experiments we consider only the fruit on
the trees for prediction of the fruit yield. We evaluate the per tree yield
prediction correlation with ground truth estimates using the square of the
Pearson correlation coefficient defined as,

$R^{2}=\bigg{(}\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{n}(x_{i}-\bar{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}})}\bigg{)}^{2},$
(1)

where $n$ is the total number of trees in the experiment, $x$ and $y$ are the
predicted and ground-truth yield estimates, and $\bar{x}$ and $\bar{y}$ are
the mean of the predicted and ground-truth yield estimates. Two simple yield
estimation methods were compared: detect-count and filter-detect-count. The R2
values are reported in Table IV.

TABLE IV: Tree Yield Correlation Method | Front R2 | Back R2 | Front+Back R2
---|---|---|---
detect-count | 0.584 | 0.635 |  0.754
filter-detect-count | 0.603 | 0.677 |  0.793

The first method for yield estimation, detect-count, simply detects the fruit
in an image and estimates the counts of fruit labeled as fruit on the tree
with a confidence of at least 0.3. Detection is applied to both the front and
back images, and yield is estimated separately for each. We made the
assumption that due to the high amount of foliage on the trees, fruits that
are visible in one view will be occluded in the other view. As such, the sum
of the predicted counts for each view of the tree are used as the predicted
fruit yield.

An analysis of large prediction errors showed that many images contained parts
of neighboring trees in the image, which attributed to higher fruit counts.
The second method, filter-detect-count, aims to address this issue by using a
two-stage detection approach to filter out neighboring trees. The first stage
uses a separate model to detect all trees and their canopies. Then, the
central most detection was taken as input to the second stage and passed to a
YOLO model for fruit detection. The improved method correctly predicted four
out of the five top-producing trees with an error of only -1.49% on the total
yield estimation of the orchard as shown in Fig. 5.

Figure 5: The total predicted yield and the difference of actual and predicted
yield for 187 sample citrus trees of varying species and maturity obtained
using the filter-detect-count method. We used in-field hand counts acquired by
technicians as the ground-truth yield.

##  VII Conclusion and Future Work

In this work we introduced CitDet, a new dataset for citrus detection in
orchards prone to citrus greening. The dataset was created with the intention
of being diverse and unbiased, it contains many different varieties of citrus
at different stages of maturity and health. By distinguishing between fruit on
the ground and fruit on the trees, we showed that it is possible to identify
fruit that contributes to yield via a single model. With this annotated
collection of citrus, we aim to increase the comparability of fruit detection
algorithms and advance research on this topic. The results of the object
detection benchmarks indicate that CitDet is challenging for modern detectors
and there are many opportunities for future research and improvement. In
particular, future work is necessary to improve the accuracy of detectors on
small objects in cluttered environments.

## Acknowledgments

This material is based upon work supported by the United States Department of
Agriculture (USDA) under USDA-ARS CRIS #6034-21000-018-000-D,
#6066-21310-006-000-D, and USDA-ARS Non-Assistance Cooperative Agreement
#6066-21310-005-061-S. The authors thank members of the Hulse-Kemp Lab
including Dr. Keo Corak, Dr. Emily Delorean, Ashley Schoonmaker, Grant
Billings, and Cassie Newman for assistance in image annotation and USDA-ARS
USHRL technicians Jefferson Shaw and Darren Cole for assistance in image
collection.

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