YOLOv10-Enabled IoT Robot Car for Accurate Disease Detection in Strawberry Cultivation

Abstract

This study addresses the growing need for effective disease management in strawberry cultivation, a crop vital for global nutrition. We present an innovative approach that combines the YOLOv10 model with a Remote-Controlled Robot Car to revolutionize strawberry disease detection. Our system merges deep learning, IoT, and precision agriculture techniques to enable real-time monitoring of strawberry fields. This technology-driven solution offers a proactive and data-based method for identifying diseases early. Our findings show the potential of this advanced system to significantly improve agricultural practices and support sustainable food production. The YOLOv10n model achieved a 96.78% mAP-50 ratio for accurately locating diseased leaves. By integrating IoT capabilities, the system allows for remote control and continuous monitoring, eliminating the need for daily on-site expert inspections. This approach not only enhances disease management efficiency but also has the potential to increase crop yields and reduce pesticide use, contributing to more sustainable farming practices.