Intelligent Techniques for Nutrition Level Prediction in Guava Plants

Guava, a valuable tropical fruit crop, faces significant defect challenges due to diseases. Manual disease checks are time- consuming and error prone, leading to delayed action. Our research focuses on image processing to quickly and accurately identify guava diseases using deep learning approach with the help of Guava leaf images. This automation minimizes human intervention, enhances accuracy, and aids disease prevention. Early detection of these diseases improves crop quality and sustainability while reducing pesticide use. This Deep learning- driven approach aims to benefit farmers, consumers, and global food security by advancing guava farming practices. By using the YOLOv8 object detection model for 6 classes, we got an accuracy of 87.97%.