# O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments

arXiv: https://arxiv.org/abs/2303.04884 · HTML: https://mlsanity.com/paper/2303.04884/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 77 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://mlsanity.com/paper/2103.12340/) (2021)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)
- [Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting](https://mlsanity.com/paper/2003.13298/) (2020)
- [Algorithm Design and Integration for a Robotic Apple Harvesting System](https://mlsanity.com/paper/2203.00582/) (2022)
- [System Design and Control of an Apple Harvesting Robot](https://mlsanity.com/paper/2010.11296/) (2020)

## Built on by (most cited first, 7 of 7)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)
- [MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models](https://mlsanity.com/paper/2407.04711/) (2024)
- [S3AD: Semi-supervised Small Apple Detection in Orchard Environments](https://mlsanity.com/paper/2311.05029/) (2023)
- [Foundation Model-Based Apple Ripeness and Size Estimation for Selective Harvesting](https://mlsanity.com/paper/2502.01850/) (2025)
- [Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation](https://mlsanity.com/paper/2505.16547/) (2025)
- [AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards](https://mlsanity.com/paper/2505.14029/) (2025)

## Abstract (arXiv)

Automated apple harvesting has attracted significant research interest in recent years due to its potential to revolutionize the apple industry, addressing the issues of shortage and high costs in labor. One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard environments that involve varying lighting conditions and foliage/branch occlusions. Furthermore, clustered apples are common in the orchard, which brings additional challenges as the clustered apples may be identified as one apple. This will cause issues in localization for subsequent robotic operations. In this paper, we present the development of a novel deep learning-based apple detection framework, Occluder-Occludee Relational Network (O2RNet), for robust detection of apples in such clustered environments. This network exploits the occuluder-occludee relationship modeling head by introducing a feature expansion structure to enable the combination of layered traditional detectors to split clustered apples and foliage occlusions. More specifically, we collect a comprehensive apple orchard image dataset under different lighting conditions (overcast, front lighting, and back lighting) with frequent apple occlusions. We then develop a novel occlusion-aware network for apple detection, in which a feature expansion structure is incorporated into the convolutional neural networks to extract additional features generated by the original network for occluded apples. Comprehensive evaluations are performed, which show that the developed O2RNet outperforms state-of-the-art models with a higher accuracy of 94% and a higher F1-score of 0.88 on apple detection.