Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting
Year: 2025
Type: article
Abstract: Introduction: Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and YOLO provide accurate 2D detection, they require large annotated datasets and high computational resources, and often lack the precise 3D localisation required for robotic picking. Methods: This study proposes an enhanced K-Means clustering segmentation algorithm integrated with a stereo-vision system for accurate 3D apple localisation. Multi-feature fusion combining colour, morphology, and texture descriptors was applied to improve segmentation robustness. A block-matching stereo model was used to compute disparity and derive 3D coordinates. The method was evaluated against Faster R-CNN, YOLOv7, Mask R-CNN, SSD, DBSCAN, MISA, and HCA using metrics including Recognition Accuracy (RA), mean Average Precision (mAP), Mean Coordinate Deviation (MCD), Correct Recognition Rate (CRR), Frames Per Second (FPS), and depth-localisation error. Results: The proposed method achieved >91% detection accuracy and <1% localisation error across challenging orchard conditions. Compared with Faster R-CNN, it maintained higher RA and lower MCD under high fruit overlap and variable lighting. Depth estimation achieved errors between 0.4%-0.97% at 800-1100 mm distances, confirming high spatial accuracy. The proposed model exceeded YOLOv7, SSD, FCN, and Mask R-CNN in F1-score, mAP, and FPS during complex lighting, occlusion, wind disturbance, and dense fruit distributions. Discussion and Conclusion: The clustering-based stereo-vision framework provides stable 3D localisation and robust segmentation without large training datasets or high-performance hardware. Its low computational demand and strong performance under diverse orchard conditions make it suitable for real-time robotic harvesting. Future work will focus on large-scale orchard deployment, parallel optimisation, and adaptation to additional fruit types.
Source: Frontiers in Plant Science
Authors: Jianxia Wang, Wenbing Sun
Institution: Tarim University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4416770281