关键词: 植物病害, 深度学习, Faster R-CNN, 迁移学习, 病害检测

Abstract: Plant diseases can have a disastrous impact on food safety. They can directly lead to a significant decrease in the quality and yield of crops. Therefore, early identification of plant diseases is very important. Traditional crop disease diagnosis requires very high professional knowledge, not only timeconsuming and laborious, but also inefficient. In this paper, we use the method of deep learning to develop the Faster R-CNN network model based on TensorFlow, and taking potato leaves as research samples. We expanded the image of potato leaves with early blight, late blight and health by local enhancement method. The COCO initial weight was applied for transfer learning, and the influence of data categories on model detection results was explored. The results showed that with the increase of training data categories, the performance of the model would be slightly reduced. At the same time, we also trained YOLOv3 and YOLOv4 network to compare with this model. The test results showed that the performance of our proposed Fater R-CNN model was better than other network model, and the best accuracy of the model reached 99.5% after testing. This study can provide technical support for potato disease detection.

Key words: plant disease, deep learning, Faster R-CNN, transfer learning, disease detection