A novel method for online sex sorting of silkworm pupae (Bombyx mori) using computer vision combined with deep learning

文献类型： 外文期刊

第一作者： Guo, Feng

作者： Guo, Feng;Qin, Wei;Fu, Xinglan;Zhao, Chunjiang;Li, Guanglin;Tao, Dan;Zhao, Chunjiang

作者机构：

关键词： silkworm pupae; crossbreeding; sex identification; computer vision; cascaded spatial channel attention

期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE （ 影响因子：3.5； 五年影响因子：4.2 ）

ISSN： 0022-5142

年卷期： 2025 年 105 卷 8 期

页码：

收录情况： SCI

摘要： BACKGROUNDSilkworm pupae (SP), the pupal stage of an edible insect, have strong potential in the food, medicine, and cosmetic industries. Sex sorting is essential to enhance nutritional content and genetic traits in SP crossbreeding but it remains labor intensive and time consuming. An intelligent method is needed urgently to improve efficiency and productivity.RESULTSTo address the problem, an automatic SP sex-separation system was developed based on computer vision and deep learning. Specifically, based on gonad features, a novel real-time SP sex identification model with cascaded spatial channel attention (CSCA) and G-GhostNet (GPU-Ghost Network) was developed, which can capture regions of interest and achieve feature diversity efficiently. A new loss function was proposed to reduce model complexity and avoid overfitting in the training. In comparison with benchmark methods on the test set, the new model achieved superior performance with an accuracy of 96.48%. The experimental sorting accuracy for SP reached 95.59%, validating the effectiveness of the novel gender-separation strategy.CONCLUSIONThis research presents a practical method for online SP gender separation, potentially aiding the production of high-quality SP. (c) 2025 Society of Chemical Industry.

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