Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments

Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li and Pei Wang ()
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Qi Niu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Wenjun Ma: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Rongxiang Diao: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Wei Yu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Chunlei Wang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Hui Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Lihong Wang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Chengsong Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Pei Wang: College of Engineering and Technology, Southwest University, Chongqing 400715, China

Agriculture, 2025, vol. 15, issue 10, 1-15

Abstract: The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper ( Zanthoxylum schinifolium ) as a specialty economic crop.

Keywords: green Sichuan pepper; YOLO; k-means++; cutting-point localization; cluster object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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