Automated detection of larval stages of the black soldier fly (Hermetia illucens Linnaeus) through deep learning augmented with optical flow

The black soldier fly (BSF) Hermetia illucens has garnered significant attention for its potential in sustainable waste management, nutrient recycling, and the production of valuable resources such as protein-rich animal feed and biofuels. Traditional mass production methods remain labor-intensive and error-prone, needing automated solutions. A critical challenge is the precise identification of BSF different life stages which is essential for optimizing feeding strategies, harvesting, and overall system efficiency. This study explores the use of deep learning, combined with optical flow analysis, to identify BSF life stages, particularly larvae, prepupae, and pupae. A Convolutional Neural Network (CNN) model was employed for real-time BSF larval stages detection. Training, validation, and test were performed on a comprehensive custom dataset of 2130 images. Evaluation metrics including precision, recall, and mean Average Precision (mAP) were assessed. Overall, the CNN model showed a precision of 0.96, a recall of 0.95, and a mAP@0.5 of 0.97 on the test set, confirming its generalization capability and effectiveness in real-world scenarios. The integration of optical flow enhanced the model’s performance by leveraging prior knowledge of motor activity, particularly for identifying and correcting false positives in pupae classification. Automated identification of BSF larval stages optimizes resource management, reduces operational costs, and enhances the economic viability of BSF-based systems. The proposed system extends beyond terrestrial concerns, with potential implications for bioregenerative life-support systems, a promising space technology.