Efficient and Precise Disease Detection in Tomato Plants Using YOLOv7 for Automated Recognition

Tomato plants are very vulnerable to diseases that leads to it being spoiled or resulting in bad yield quality which causes lot of trouble to the farmers. Usually, the farmers are trained to identify the diseases using manual observation, which is proven to be inefficient since it requires much training and experience to be able to identify these diseases at a glance. Therefore, we are proposing this research, an efficient framework for tomato plant disease detection using automated recognition using YOLOv7 deep learning model. It uses superior convolutional neural network architecture, which identifies and classifies multiple leaf diseases accurately under complex field conditions. Thus, it is more faster and accurate than traditional methods. This work aims to implement technical developments like AI, Object detection to make the task of identifying diseases easily and efficiently.