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

Niu, Qi; Ma, Wenjun; Diao, Rongxiang; Yu, Wei; Wang, Chunlei; Li, Hui; Wang, Lihong; Li, Chengsong; Wang, Pei

doi:10.3390/agriculture15101079

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

by

Qi Niu

^1,2

,

Wenjun Ma

¹,

Rongxiang Diao

¹,

Wei Yu

¹,

Chunlei Wang

¹,

Hui Li

^1,2,

Lihong Wang

^1,2,

Chengsong Li

^1,2 and

Pei Wang

^1,2,*

¹

College of Engineering and Technology, Southwest University, Chongqing 400715, China

²

Key Laboratory of Agricultural Equipment in Hilly and Mountainous Areas, Southwest University, Chongqing 400715, China

^*

Author to whom correspondence should be addressed.

Agriculture 2025, 15(10), 1079; https://doi.org/10.3390/agriculture15101079 (registering DOI)

Submission received: 17 March 2025 / Revised: 20 April 2025 / Accepted: 14 May 2025 / Published: 16 May 2025

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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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

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MDPI and ACS Style

Niu, Q.; Ma, W.; Diao, R.; Yu, W.; Wang, C.; Li, H.; Wang, L.; Li, C.; Wang, P. Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments. Agriculture 2025, 15, 1079. https://doi.org/10.3390/agriculture15101079

AMA Style

Niu Q, Ma W, Diao R, Yu W, Wang C, Li H, Wang L, Li C, Wang P. Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments. Agriculture. 2025; 15(10):1079. https://doi.org/10.3390/agriculture15101079

Chicago/Turabian Style

Niu, Qi, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li, and Pei Wang. 2025. "Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments" Agriculture 15, no. 10: 1079. https://doi.org/10.3390/agriculture15101079

APA Style

Niu, Q., Ma, W., Diao, R., Yu, W., Wang, C., Li, H., Wang, L., Li, C., & Wang, P. (2025). Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments. Agriculture, 15(10), 1079. https://doi.org/10.3390/agriculture15101079

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

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