Wheat Powdery Mildew Spore Images Segmentation Based on U-Net

Abstract

In recent years, there have been significant development in deep learning technology, which has also promoted the development of image segmentation. Wheat powdery mildew is a common crop disease, which is very harmful to crops. The segmentation task of wheat powdery mildew spore images is important for the target spores identification and spores counting. In this paper, an improved framework based on U-Net is proposed, comparing with original U-Net, we add the pyramid pooling module after the 1024-channel feature map in the down-sampling part to extract different sizes pooling feature and fuse into a global feature map, and adjust some skip connections in original U-Net to obtain more features that are effective for spore images segmentation, experiment shows that the improved U-Net structure has better segmentation performance than U-Net, and the segmentation miou (mean intersection over union) has reached 91.4% in wheat powdery mildew spore image dataset, which proves that our proposed architecture is effective and competitive in the wheat powdery mildew spore segmentation task.