Segmentation of Corn Leaf Disease Based on Fully Convolution Neural Network

Aiming at the problem that traditional computer vision is susceptible to different illumination and complex background in the segmentation of corn leaf disease, a method of segmentation of corn leaf disease based on full convolution neural network is proposed. Firstly, pre-processing and data enhancement are performed on the acquired images to establish a training set and a test set for deep learning. Secondly, the training centralized image is input into the FCN, and the feature map is obtained through multiple convolution, pooling, and activation layers; The feature map is upsampled to obtain a feature map of the same size as the input image. Finally, the resolution of the segmented image is restored by the process of deconvolution, and the segmentation result is output. This method was used to segment the common corn leaf disease images. The results show that the method has better segmentation effect and the segmentation accuracy is over 96%.