An Improved YOLOv8n-Based Method for Detecting Rice Shelling Rate and Brown Rice Breakage Rate

Zhaoyun Wu, Yehao Zhang, Zhongwei Zhang (), Fasheng Shen, Li Li, Xuewu He, Hongyu Zhong and Yufei Zhou
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Zhaoyun Wu: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Yehao Zhang: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Zhongwei Zhang: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Fasheng Shen: Shandong Alesmart Intelligent Technology Co., Ltd., Jinan 250014, China
Li Li: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Xuewu He: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Hongyu Zhong: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Yufei Zhou: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China

Agriculture, 2025, vol. 15, issue 15, 1-25

Abstract: Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting.

Keywords: paddy grains; lightweight detection; YOLOv8n; shelling rate; brown rice breakage rate (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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