A Detection Line Counting Method Based on Multi-Target Detection and Tracking for Precision Rearing and High-Quality Breeding of Young Silkworm ( Bombyx mori )

Zhenghao Li, Hao Chang, Mingrui Shang, Zhanhua Song, Fuyang Tian, Fade Li, Guizheng Zhang, Tingju Sun, Yinfa Yan () and Mochen Liu ()
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Zhenghao Li: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Hao Chang: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Mingrui Shang: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Zhanhua Song: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Fuyang Tian: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Fade Li: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Guizheng Zhang: Guangxi Institute of Sericulture Science, Nanning 530007, China
Tingju Sun: Shandong Guangtong Silkworm Rearing Co., Ltd., Weifang 262550, China
Yinfa Yan: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Mochen Liu: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China

Agriculture, 2025, vol. 15, issue 14, 1-22

Abstract: The co-rearing model for young silkworms ( Bombyx mori ) utilizing artificial feed is currently undergoing significant promotion within the sericulture industry in China. Within this model, accurately counting the number of young silkworms serves as a crucial foundation for achieving precision rearing and high-quality breeding. Currently, manual counting remains the prevalent method for enumerating young silkworms, yet it is highly subjective. A dataset of young silkworm bodies has been constructed, and the Young Silkworm Counting (YSC) method has been proposed. This method combines an improved detector, incorporating an optimized multi-scale feature fusion module and the Efficient Multi-Scale Attention Fusion Cross Stage Partial (EMA-CSP) mechanism, with an optimized tracker (based on ByteTrack with improved detection box matching), alongside the implementation of a ‘detection line’ approach. The experimental results demonstrate that the recall, precision, and average precision ( AP 50:95 ) of the improved detection algorithm are 87.9%, 91.3% and 72.7%, respectively. Additionally, the enhanced ByteTrack method attains a multiple-object tracking accuracy ( MOTA ) of 88.3%, an IDF 1 of 90.2%, and a higher-order tracking accuracy ( HOTA ) of 78.1%. Experimental validation demonstrates a counting accuracy exceeding 90%. The present study achieves precise counting of young silkworms in complex environments through an improved detection-tracking method combined with a detection line approach.

Keywords: young silkworm counting; artificial feed co-rearing; overlapping detection box matching; improved ByteTrack; video analysis (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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