Enhancing Honeybee Hive Health Monitoring: Vision Transformer-Based Non-Invasive Classification

Monitoring honeybee-hive health is crucial to ensure the continuing existence of honeybee colonies, which are essential to world ecosystems and agriculture. Classical approaches to diagnosing issues in hives, such as the presence of hive beetles or a Varroa mite infestation, can be laborious and need specific expertise. In this study, we present an automated technique for classifying honeybee-hive health using Vision Transformer (ViT) models. Our dataset includes classes that indicate a range of factors, including hive beetles, ant problems, healthy bees, Varroa mite presence, hive robbery, and missing queens. We selected the ViT model for its ability to capture complex patterns in images, making it particularly suitable for the delicate and challenging task of classifying honeybee hive health. The proposed approach yielded favorable results and demonstrated higher accuracy on the testing dataset, indicating its potential to aid in the early detection and management of issues related to the health of the honeybee hive. This study makes precision apiculture better by providing a scalable, effective, and efficient way to rate the health of honeybee hives. It also has implications for improving hive maintenance and honeybee conservation campaigns.