Weakly-supervised learning method for the recognition of potato leaf diseases

As a crucial food crop, potatoes are highly consumed worldwide, while they are also sus-ceptible to being infected by diverse diseases. Early detection and diagnosis can prevent the epidemic of plant diseases and raise crop yields. To this end, this study proposed a weakly-supervised learning approach for the identification of potato plant diseases. The founda-tion network was applied with the lightweight MobileNet V2, and to enhance the learn-ing ability for minute lesion features, we modified the existing MobileNet-V2 architecture using the fine-tuning approach conducted by transfer learning. Then, the atrous convolution along with the SPP module was embedded into the pre-trained networks, which was fol-lowed by a hybrid attention mechanism containing channel attention and spatial attention submodules to efficiently extract high-dimensional features of plant disease images. The proposed approach outperformed other compared methods and achieved a superior per-formance gain. It realized an average recall rate of 91.99% for recognizing potato disease types on the publicly accessible dataset. In practical field scenarios, the proposed approach separately attained an average accuracy and specificity of 97.33% and 98.39% on the locally collected image dataset. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach.