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<!DOCTYPE html>
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<meta name="description" content="A benchmark dataset for citrus fruit detection.">
<meta name="keywords" content="computer vision, object detection and classification, agriculture, citrus">
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<title>CitDet: A Benchmark <br /> Dataset for Citrus Fruit Detection</title>
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<h1 class="title is-1 publication-title">CitDet: A Benchmark Dataset for <br/> Citrus Fruit Detection</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"> Jordan A. James<sup>1</sup>, Heather K. Manching<sup>2</sup>, Matthew R. Mattia<sup>3</sup>, Kim D. Bowman<sup>3</sup>, <br> <a href="https://genetics.sciences.ncsu.edu/people/amhulse">Amanda M. Hulse-Kemp<sup>2,4</sup></a>, and <a href="https://ranger.uta.edu/~wjbeksi">William J. Beksi<sup>1</sup></a></span>
</div>
<div class="is-size-5 publication-institutes">
<span class="institute-block">
The University of Texas at Arlington<sup>1</sup>, <a href="https://rvl.uta.edu">Robotic Vision Laboratory</a><br>
North Carolina State University<sup>2</sup>, <a href="https://breedingstars.com">Hulse-Kemp Laboratory</a><br>
Subtropical Insects and Horticulture Research Unit, USDA Agricultural Research Service<sup>3</sup><br>
Genomics and Bioinformatics Research Unit, USDA Agricultural Research Service<sup>4</sup>
</span>
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<span>Preprint</span>
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<span>Presentation</span>
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<a href="https://github.com/robotic-vision-lab/CitDet-A-Benchmark-Dataset-For-Citrus-Fruit-Detection" class="external-link button is-normal is-rounded is-dark">
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<span>Source Code</span>
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<div class="container is-max-desktop" id="Abstract">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
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 to the
agricultural research community, yet there is an absence of work,
particularly involving public datasets of citrus affected by
Huanglongbing (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.
</p>
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<h2 class="title is-3">Dataset</h2>
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<p>
The <strong>CitDet</strong> dataset is composed of images captured
at the USDA Agricultural Research Service Subtropical Insects and
Horticulture Research Unit in Fort 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. The dataset consists
of over 32,000 bounding box annotations for fruit instances
contained in 579 high-resolution images. While imaging the trees,
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.
</p>
<p>
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, the dataset 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 60 different varieties
of citrus. All images include a timestamp, tree ID, and the side of
the tree imaged in the file name. To best serve the broader
research communities, <strong>CitDet</strong> is also hosted on the
<a href="https://doi.org/10.15482/USDA.ADC/1529611">USDA Ag Data Commons</a>.
</p>
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</div>
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<br>
<div class="container is-max-desktop content" id="Citation">
<!-- Citation -->
<h2 class="title is-3">Citation</h2>
<p>
If you find this project useful, then please consider citing both our
paper and dataset.
</p>
<pre><code>@article{james2024citdet,
title={CitDet: A Benchmark Dataset for Citrus Fruit Detection},
author={James, Jordan A and Manching, Heather K and Mattia, Matthew R and Bowman, Kim D and Hulse-Kemp, Amanda M and Beksi, William J},
journal={IEEE Robotics and Automation Letters (RA-L)},
pages={},
year={2024}
}</code></pre>
<pre><code>@data{T8/QFVHQ5_2024,
title={{CitDet}},
author={James, Jordan A and Manching, Heather K and Mattia, Matthew R and Bowman, Kim D and Hulse-Kemp, Amanda M and Beksi, William J},
publisher={Texas Data Repository},
version={V1},
url={https://doi.org/10.18738/T8/QFVHQ5},
doi={10.18738/T8/QFVHQ5},
year={2024}
}</code></pre>
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<br>
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<h2 class="title is-3">License</h2>
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<p>
The source code associated with this project is licensed under the
<a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License, Version 2.0</a>.
The <strong>CitDet</strong> dataset is available for non-commercial use under the
<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("CC BY-NC-SA 4.0")</a>.
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This means you are free to borrow the
<a href="https://github.com/robotic-vision-lab/citdet">source code</a>
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