Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning

Abstract

The growing interest in insect farming as a sustainable protein alternative has given rise to the commercial production of key species like the Black Soldier Fly (BSF), primarily for use in livestock, fish, and pet nutrition. Despite the heightened interest in BSF production, there is a need for increased efficiency, particularly in the context of large-scale measurement of various traits for selective breeding as well as management optimization. The unique insect production systems, coupled with the challenges posed by their small size, fragility, and metamorphic life cycle underscores the necessity for innovative approaches to streamline production.

This study explores the potential of computer vision (CV) in predicting the larval sex and morphological traits of BSF, offering a non-invasive, rapid, and automated method for trait measurement. The study explores algorithms utilizing You-Only-Look-Once (YOLOv8) in detection and segmentation, ResNet for feature extraction and classification, and regression analysis mechanisms. We assess the ability of our models to predict larval weight from images through morphometric weight prediction and CNN-regression approaches.

A notable contribution of this study is the pioneering effort to classify BSF larval sex using CV and deep learning (DL). In the analysis of larval weight prediction, a coefficient determination (R²) of up to 0.80 between measured and predicted weight was achieved using the morphometric weight prediction approach, along with an R² of 0.71 through the CNN-regression approach. Additionally, the sex prediction module demonstrated an F1 score of 0.75 and a prediction accuracy of 74 %. These results underscore the feasibility of leveraging CV techniques for predicting the sex and body traits of BSF larvae, representing a significant advancement toward the automation of selective breeding in the context of insect farming.