# Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

arXiv: https://arxiv.org/abs/1610.08120 · HTML: https://mlsanity.com/paper/1610.08120/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

## Built on by (most cited first, 19 of 19)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning](https://mlsanity.com/paper/2205.09995/) (2022)
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- [O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments](https://mlsanity.com/paper/2303.04884/) (2023)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [End-to-end deep learning for directly estimating grape yield from ground-based imagery](https://mlsanity.com/paper/2208.02394/) (2022)
- [Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation](https://mlsanity.com/paper/2106.04208/) (2021)
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- [Few-Shot Fruit Segmentation via Transfer Learning](https://mlsanity.com/paper/2405.02556/) (2024)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting](https://mlsanity.com/paper/2105.03832/) (2021)
- [Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation](https://mlsanity.com/paper/2109.07247/) (2021)
- [Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots](https://mlsanity.com/paper/2306.13518/) (2023)
- [Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction](https://mlsanity.com/paper/2312.02254/) (2023)
- [Robust soybean seed yield estimation using high-throughput ground robot videos](https://mlsanity.com/paper/2412.02642/) (2024)
- [Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones](https://mlsanity.com/paper/2501.02344/) (2025)
- [Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse](https://mlsanity.com/paper/2505.00995/) (2025)
- [AGRO: An Autonomous AI Rover for Precision Agriculture](https://mlsanity.com/paper/2505.01200/) (2025)

## Abstract (arXiv)

Ground vehicles equipped with monocular vision systems are a valuable source of high resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general purpose image segmentation approach is used, including two feature learning algorithms; multi-scale Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the Watershed Segmentation (WS) and Circular Hough Transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of 0.791 was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of 0.858. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of r2=0.826.