Description
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 programs, but traditional methods for detecting pests are time- and labor-intensive and require taxonomic expertise. New, automated methods using computer vision have the potential to improve accuracy and speed of detection, but often struggle to differentiate between beetle species, which tend to be small and morphologically similar. Our research centers on five economically significant beetle species, referred to as the 'Beetle Byte Quintet,' and proposes a novel methodology leveraging Vision Transformers (ViT) to enhance the precision and robustness of their classification. The method involves using an image profiling technique to capture morphological characteristics like body shape, color and exoskeleton structures that are key for distinguishing between species. By utilizing this species profiling, the ViT model achieved an accuracy rate of over 99.34% during training and 96.57% during testing. These findings highlight the model’s ability to generalize and maintain precision with new unseen data surpassing traditional computer vision algorithms significantly. The integration of ViT can help enable real time monitoring and is adaptable to a range of pest monitoring solutions for large scale storage settings which addresses the complexities of environments. This AI driven approach not only simplifies species identification but also promotes accurate and targeted pest control practices leading to reduced economic losses and improved food security.
A subsample of images used in the model are included here for Rhyzopertha dominica (lesser grain borer), Sitophilus zeamais (maize weevil), Tribolium castaneum (red flour beetle), Cryptolestes ferrugineus (rusty grain beetle), and Oryzaephilus surinamensis (sawtoothed grain beetle). Custom MatLab code and a data descriptor README are also included.