文献类型： 外文期刊

作者： Shi, Hongkang ¹ ; Zhu, Shiping ² ; Chen, Xiao ¹ ; Zhang, Jianfei ¹ ;

作者机构： 1.Sichuan Acad Agr Sci, Sericultural Res Inst, Nanchong, Sichuan, Peoples R China

2.Southwest Univ, Coll Engn Technol, Chongqing, Peoples R China

3.Southwest Univ, Yibin Acad, Yibin, Sichuan, Peoples R China

关键词： Identification of day instar; CSPNet; feature fusion; image attention mechanism; silkworm

期刊名称：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS （ 影响因子：2.0； 五年影响因子：1.9 ）

ISSN： 1064-1246

年卷期： 2023 年 45 卷 5 期

页码：

收录情况： SCI

摘要： Identifying the day instar of silkworms is a fundamental task for precision rearing and behavioral analysis. This study proposes a new method for identifying the day instar of adult silkworms based on deep learning and computer vision. Images from the first day of instar 3 to the seventh day of instar 5 were photographed using a mobile phone, and a dataset containing 7, 000 images was constructed. An effective recognition network, called CSP-SENet, was proposed based on CSPNet, in which the hierarchical kernels were adopted to extract feature maps from different receptive fields, and an image attention mechanism (SENet) was added to learn more important information. Experiments showed that CSP-SENet achieved a recognition precision of 0.9743, a recall of 0.9743, a specificity of 0.9980, and an F1-score of 0.9742. Compared to state-of-the-art and related networks, CSP-SENet achieved better recognition performance with the advantage of computational complexity. The study can provide theoretical and technical references for future work.