Abstract
The current convolution neural network approaches have attracted extensive interest because the performance is better than that of conventional machine learning methods in the plant disease recognition. However, there are still facing challenges. For instance, the image background sometime is complex, and the model can detect plant lesions, but it is difficult to use and detect the specific pest position. The high complexity of the model is not conducive to the deployment and development of mobile software. Even the dataset has problems such as labeling errors and few positive or negative samples, which restrict the development of disease recognition. In this study, we investigate the deep convolution networks based on deep transfer learning for plant disease recognition. We propose a model called as Selective Kernel MobileNet (SK-MobileNet), which is lightweight enough to greatly reduce the computing cost when deployed to servers. Experimental results show that the proposed approach reaches the accuracy of 99.28% in the public dataset. The proposed approach illustrates a significant increase in the efficiency with the lower complexity compared to other existing methods.