STN-MobileNetV2: A Hybrid Lightweight Deep Learning Model for Maize Disease Classification

Agriculture occupies an essential role because of the demand for food, and it is a crucial source of human income in many countries, especially in developing countries. In the ‎world, China is a large agricultural country and many people ‎rely on agricultural production for a ‎living. Maize is widely cultivated as a kind of the major dominant crop, while the diseases of ‎maize ‎not only influence the maize plantation but also the economic development. Severe maize diseases ‎may even result in no ‎harvest of grains. Thereupon, looking for an accurate, fast, automatic, and ‎low-cost approach to conduct maize disease recognition is ‎of great realistic importance. In this study, we put forward a novel image-based network architecture for maize disease identification. The proposed network integrates a Spatial Transformer Network (STN) with MobileNetV2, forming a hybrid architecture termed STN-MobileNetV2. This model leverages MobileNetV2’s pre-trained efficiency while enhancing spatial invariance through STN, enabling robust recognition of maize disease types.We also improved the Focal-Loss (FL) function to enable it to handle multi-class problems and keep more attention on minor lesion characteristics. When benchmarked against other state-of-the-art (SOTA) techniques, the presented approach exhibits superior efficacy. Specifically, it attains an average recognition accuracy of 88.00% and a specificity of 92.00% using the publicly available dataset.Even when eight disease types are considered, the proposed approach realizes a mean accuracy and specificity of 92.84% and95.85% on the ‎locally captured maize disease images. Results derived from the experiments demonstrate that the proposed model is highly effective in the identification of maize-related diseases.