AI-Based Image Profiling and Detection for the Beetle Byte Quintet Using Vision Transformer (ViT) in Advanced Stored Product Infestation Monitoring

Abstract

Managing beetles that infest stored products is crucial for reducing losses in harvest supply chains and improving food security and safety. Successful pest management programs require effective and timely monitoring systems; however, traditional detection methods are often time-consuming, labor-intensive, and reliant on taxonomic expertise. Automated approaches using computer vision offer improved efficiency, yet consistently struggle to distinguish between species due to the small size and morphological similarities among beetles. This study introduces an AI-based image profiling framework for detecting the “Beetle Byte Quintet”—a group of five morphologically similar and economically important stored-product beetle species: the maize weevil (Sitophilus zeamais), red flour beetle (Tribolium castaneum), rusty grain beetle (Cryptolestes ferrugineus), sawtoothed grain beetle (Oryzaephilus surinamensis), and lesser grain borer (Rhyzopertha dominica). Leveraging the capabilities of Vision Transformers (ViT), a model was trained on a manually curated, high-resolution image dataset annotated to highlight distinctive morphological traits such as body shape, coloration, and exoskeleton patterns. Through this profiling approach, the ViT model achieved a classification accuracy of 99.34% during training and 96.57% on unseen test data, demonstrating strong generalizability and outperforming traditional CNN-based classifiers. The integration of ViT enables interpretable attention maps, offering entomological insight and validation while supporting real-time detection applications. This AI-enhanced system presents a scalable solution adaptable to large-scale storage facilities, contributing to precise, targeted pest control strategies that reduce economic losses and promote sustainable food security practices.