基于图像超分辨率预处理和二次迁移学习的水稻病害识别方法OA

Aiming at the problem that the existing deep learning model has low accuracy in the recognition of rice leaf dis-eases,in this article we propose a rice disease recognition method based on image super-resolution preprocessing and two-step transfer learning.With the use of super-resolution reconstruction technology,higher quality images can be obtained,thus improving the recognition rate.By using the two-step transfer learning technology,AW modules composed of Inception blocks is introduced to construct the network model AW-Net(Add width modules to the network model).This method in-creases the network width of the model,which can effectively reduce the intra-class distance,enlarge the inter-class distance,realize the effective extraction of the features of rice leaf disease regions and improve the recognition rate.The experimental results showed that the recognition accuracy of our proposed AW-Net model was significantly improved.