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

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

The co-rearing model for young silkworms (<i>Bombyx mori</i>) 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 achievin...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhenghao Li, Hao Chang, Mingrui Shang, Zhanhua Song, Fuyang Tian, Fade Li, Guizheng Zhang, Tingju Sun, Yinfa Yan, Mochen Liu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Agriculture
Subjects:
young silkworm counting
artificial feed co-rearing
overlapping detection box matching
improved ByteTrack
video analysis
Online Access:https://www.mdpi.com/2077-0472/15/14/1524
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The co-rearing model for young silkworms (<i>Bombyx mori</i>) 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 (<i>AP</i><sub>50:95</sub>) 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 (<i>MOTA</i>) of 88.3%, an <i>IDF</i>1 of 90.2%, and a higher-order tracking accuracy (<i>HOTA</i>) 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.
ISSN:2077-0472

Similar Items