Abstract: Plant diseases threaten global food security, especially in high-demand crops like tomatoes and potatoes. Early and accurate detection is vital to minimize yield loss and maintain produce quality. Traditional methods, such as manual inspection, are often slow, error-prone, and require expert knowledge. With advancements in AI and computer vision, automated systems now enable faster and more accurate disease identification.
This project uses the YOLO v11 algorithm, an advanced real-time object detection model, to detect diseases in tomato and potato plants. YOLO v11 improves feature extraction, detection precision, and localization, even under changing lighting and noisy backgrounds. By training on a diverse dataset of healthy and diseased plant images, the system can accurately differentiate between infections.
The enhanced accuracy reduces false positives and negatives, ensuring more reliable detection. Early identification allows farmers to apply timely treatments, reducing pesticide use and preventing crop losses. Overall, this AI-powered system boosts agricultural productivity and promotes sustainable farming.
Keywords: YOLO v11, plant disease detection, tomato, potato, AI, computer vision, real-time object detection, crop management, agricultural sustainability, early intervention, precision agriculture.
|
DOI:
10.17148/IJARCCE.2025.14469
