Tomato Diseases Recognition Based on Faster RCNN

Plant diseases are a major challenge for the agricultural sector. Accurate and rapid detection of diseases in plants can greatly reduce economic losses. In this paper, we present a method based on Faster RCNN to detect tomato diseases. We combine Faster RCNN with different deep convolutional neural networks, including vgg16, resnet50, and resnet101. We trained and tested the collected tomato diseased dataset, which contained six diseased images and healthy tomato fruit images. The experimental results show that our proposed system can effectively identify different types of tomato diseases.