Smart Agriculture: Advanced Rice Disease Detection Using YOLOv10 for Enhanced Crop Health Management

Rice, an essential crop for global food security, faces significant threats from diseases that impact both yields and economic stability. While traditional methods like SVM, KNN, and CNNs have contributed to disease detection, their limitations in real-time application and scalability hinder their effectiveness in practical field scenarios. This research highlights YOLOv10 as a groundbreaking deep learning model capable of detecting rice leaf diseases with remarkable speed and accuracy. Its advanced design overcomes the shortcomings of earlier techniques, facilitating real-time diagnosis and classification of plant diseases. By ensuring timely interventions and minimizing crop damage, YOLOv10 supports sustainable farming practices and reduces agricultural losses. This innovative solution represents a significant step forward in modern, efficient crop management.