Real-Time Larval Stage Classification of Black Soldier Fly Using an Enhanced YOLO11-DSConv Model

Food waste presents a major global environmental challenge, contributing to resource depletion, greenhouse gas emissions, and climate change. Black Soldier Fly Larvae (BSFL) offer an eco-friendly solution due to their exceptional ability to decompose organic matter. However, accurately identifying larval instars is critical for optimizing feeding efficiency and downstream applications, as different stages exhibit only subtle visual differences. This study proposes a real-time mobile application for automatic classification of BSFL larval stages. The system distinguishes between early instars (Stages 1–4), suitable for food waste processing and animal feed, and late instars (Stages 5–6), optimal for pupation and industrial use. A baseline YOLO11 model was employed, achieving a mAP50-95 of 0.811. To further improve performance and efficiency, we introduce YOLO11-DSConv, a novel adaptation incorporating Depthwise Separable Convolutions specifically optimized for the unique challenges of BSFL classification. Unlike existing YOLO+DSConv implementations, our approach is tailored for the subtle visual differences between larval stages and integrated into a complete end-to-end system. The enhanced model achieved a mAP50-95 of 0.813 while reducing computational complexity by 15.5%. The proposed system demonstrates high accuracy and lightweight performance, making it suitable for deployment on resource-constrained agricultural devices, while directly supporting circular economy initiatives through precise larval stage identification. By integrating BSFL classification with real-time AI, this work contributes to sustainable food waste management and advances intelligent applications in precision agriculture and circular economy initiatives. Additional supplementary materials and the implementation code are available at the following link: , , .