Prototype and machine learning integration for varroa mite detection in bees.

Abstract

This report presents the development and implementation of a prototype system for Varroa mite detec- tion in bees, leveraging the integration of machine learning techniques and dual-camera setup. Varroa mites (Varroa destructor) are parasitic mites that infest honeybee colonies, causing significant damage to hive health and contributing to colony losses. Traditional detection methods, relying on manual in- spection and sampling, are laborious, time-consuming, and often result in delays in identifying and managing infestations. To address these challenges, our approach utilizes a dual-camera setup: one camera for live streaming and the other for Varroa mite detection. The first phase of our project involved understanding the characteristics and life cycle of Varroa mites. Extensive research and literature review provided the necessary foundation to develop a ma- chine learning model capable of accurately identifying Varroa mites from images captured by the dedicated detection camera. A diverse dataset of annotated images was compiled for training and validation purposes. For the development of the machine learning model, a Convolutional Neural Network (CNN) ar- chitecture was employed, leveraging its ability to effectively learn intricate features from images. The model underwent several iterations of training and fine-tuning, achieving accuracy of 85.2% and robustness in Varroa mite detection. Through a rigorous evaluation process, the model demonstrated good performance, surpassing the capabilities of human visual inspection. In the implementation phase, the two cameras were intergrated into a cohesive system. The live streaming camera provided real-time video feed of the beehive, while the detection camera con- tinuously captured images for Varroa mite identification.The system incorporated image processing algorithms to extract relevant features and feed them into the trained machine learning model. Upon detection of a Varroa mite, the system triggered an alert mechanism utilizing GSM technology, send- ing an immediate notification to the beekeeper or farmer. Field testing was conducted to validate the functionality and effectiveness of the prototype system. The system was deployed in Kawempe , and data were collected over an extended period. The results demonstrated the system’s ability to detect Varroa mites in real-time, providing early warning signs of infestations. The alerts received by the beekeeper(Mr Kaddu John) enabled timely intervention and implementation of appropriate mitigation strategies, minimizing the potential damage caused by Varroa mite infestations.