Investigating Performance MLP-Mixer and gMLP for Crop Leaf Diseases

Early recognition of plant diseases is crucial, and one practical approach is using deep learning models. Models, such as MLP-Mixer and gMLP, based on multi-layer perceptron, offer a compelling option due to their simple architecture. This study aimed to assess the performance of these models in classifying crop leaf diseases. Our findings reveal that both MLP-Mixer and gMLP models, with their similar architectures, exhibit promising performance. We conducted tests using public potato and wheat datasets to evaluate their classification performance. Furthermore, we incorporated gradient centralization during training to enhance the models’ generalization performance. The results indicate that both MLP-Mixer and gMLP achieved classification performance above 0.9100 for both datasets. Specifically, MLP-Mixer achieved 0.9819, and gMLP achieved 0.9873 for potato leaf diseases, while for wheat leaf diseases, MLP-Mixer achieved 0.9121, and gMLP achieved 0.9189. These outcomes emphasize the potential of these models in classifying and identifying diseases in crop leaves.