Oligonychus detection Using YOLO ALGORITHM

Abstract

Olygonichus is a widespread crop disease causing concern for farmers globally. Traditional methods have proven ineffective, but recent advances in AI and drone technology offer promise in controlling it. This discussion focuses on employing AI-powered drones for Olygonichus disease management, potentially boosting crop yields and food security. The YOLO Algorithm is used for accurate disease detection. With this algorithm, drones can both identify and treat Olygonichus disease: 1. Identification: Trained on images of affected crops, the YOLO algorithm enables drones to swiftly recognize Olygonichus disease. It marks affected areas by drawing boxes around them. 2. Treatment: Equipped with specialized tools, drones can administer targeted treatments to afflicted crops. For instance, they can carry sprayers to apply pesticides directly to affected plants. This precise targeting reduces the need for broad treatment and minimizes harm to unaffected crops. By merging the YOLO algorithm’s identification capabilities with the treatment delivery potential of drones, farmers can efficiently manage Olygonichus disease. This targeted approach safeguards overall crop yield and enhances food security