Affiliations: [a]
Sericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong, Sichuan, China
| [b]
College of Engineering Technology, Southwest University, Chongqing, China
| [c]
Yibin Academy of Southwest University, Yibin, Sichuan, China
Abstract: 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.
Keywords: Identification of day instar, CSPNet, feature fusion, image attention mechanism, silkworm
