# CitDet: A Benchmark Dataset for Citrus Fruit Detection

arXiv: https://arxiv.org/abs/2309.05645 · HTML: https://mlsanity.com/paper/2309.05645/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://mlsanity.com/paper/2106.00666/) (2021)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion](https://mlsanity.com/paper/1804.00307/) (2018)
- [Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting](https://mlsanity.com/paper/2003.13298/) (2020)
- [A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization](https://mlsanity.com/paper/2112.01793/) (2021)

## Built on by (most cited first, 5 of 5)

- [Few-Shot Fruit Segmentation via Transfer Learning](https://mlsanity.com/paper/2405.02556/) (2024)
- [Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits](https://mlsanity.com/paper/2411.00967/) (2024)
- [HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection](https://mlsanity.com/paper/2508.21135/) (2025)
- [CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting](https://mlsanity.com/paper/2601.00207/) (2026)
- [Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels](https://mlsanity.com/paper/2607.14934/) (2026)

## Abstract (arXiv)

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 to 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.