MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases
Weijian Hu, Jie Fan, Yongxing Du, Baoshan Li, Naixue Xiong, Ernst Bekkering
Abstract
Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC-ResNet) model for fine-grained disease identification in the system. MDFC-ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC-ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.