Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning - Zhu, Jiajun; Cheng, Man; Wang, Qifan; Yuan, Hongbo; Cai, Zhenjiang (Frontiers in Plant Science, 2021)
The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the detection rate is low resulting in inefficiency. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU in the original YOLOv3 network is replaced with GIOU. In addition, we also add the SPP module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The black rot of grape leaves from the Plant Village dataset are used in the simulations. The results show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.