Silkworm-Smart: An AI-Powered Platform for Instance Segmentation and Precision Management in Large-Scale Factory-Based Silkworm Rearing

Automated rearing of silkworms (Bombyx mori) using artificial feed in large-scale factory environments represents a crucial step toward the modernization of sericulture in China. Timely and accurate monitoring of silkworm growth and feeding conditions is critical for optimizing rearing management in factory-based systems. This study presents Silkworm-Smart, an integrated intelligent platform that combines machine vision and deep learning to enable automated monitoring of silkworm rearing. At its core is Silkworm-AI, an instance segmentation model specifically designed to detect and segment silkworms and residual artificial feed. Developed upon the YOLOv8s-seg framework, Silkworm-AI incorporates three enhanced modules—Large Selective Kernel Network (LSKNet), Global-to-Local Spatial Aggregation (GLSA), and Context-Guided Downsampling (CGD)—to improve feature extraction and segmentation accuracy. Silkworm-AI achieved outstanding performance, with [email protected] scores of 0.971 and 0.967 for silkworm detection and segmentation, and 0.787 and 0.798 for residual feed detection and segmentation, respectively—outperforming seven state-of-the-art deep learning models. Based on model outputs, the platform automatically extracts and visualizes key rearing indicators such as silkworm count, average size, uniformity, size increment, survival rate, and residual feed area. Furthermore, a machine learning–based regression model was developed to predict cocoon yield using these image-derived indicators, achieving R2 values of 0.713 and 0.835 for the 4th and 5th instars, respectively. The Silkworm-Smart platform facilitates data-driven decision-making for rearing management, feed adjustment, and production planning, and holds significant promise for advancing automation, precision control, and sustainability in intelligent factory-based sericulture.