<![CDATA[Maize production remains vulnerable to foliar diseases such as Gray Leaf Spot, Common Rust, and Northern Leaf Blight, which can impair growth and reduce crop yields. Manual disease identification is time-consuming, labor-intensive, and reliant on the observer's expertise, necessitating automated detection systems based on deep learning. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are widely used for plant disease classification, a direct comparison between ViT-B/16 and MambaOut Tiny under identical training conditions remains rare. This study compares the two models using the PlantVillage dataset, which comprises 4,188 maize leaf images across four classes. Both models were fine-tuned using ImageNet-1K pre-trained weights, the AdamW optimizer, a learning rate of 1e-4, a batch size of 16, and 10 epochs. The results demonstrate that MambaOut Tiny achieved an accuracy of 97% and a macro-average F1-score of 0.96, outperforming ViT-B/16, which achieved 95% accuracy and an F1-score of 0.93. Additionally, MambaOut Tiny features a lower parameter count (30 million versus 86 million) and faster inference time (0.01262 seconds per image). These findings indicate that MambaOut Tiny is more efficient for precision agriculture systems with limited computational resources]]>
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