Maize is a vital global crop, but its productivity is often threatened by plant diseases, highlighting the need for precise and timely diagnostic methods. Traditional manual inspection is inefficient and prone to errors, motivating the development of automated solutions. Recent advances in computer vision and deep learning have enabled effective automated plant disease diagnosis. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown promise in plant disease classification, CNNs struggle to capture global contextual information, and ViTs require large datasets and high computational resources. Inspired by mixture-of-experts (MoE) architectures, we propose a lightweight hybrid model that integrates CNN and ViT components, adaptively emphasizing local or global features based on input characteristics. Evaluated on a novel, real-world dataset of full maize plant images, our approach achieves 99.90% classification accuracy, significantly outperforming state-of-the-art baselines such as MobileViT, PiT, EdgeNeXt, and DeiT. These results demonstrate that lightweight hybrid architectures can deliver high-performance disease diagnosis suitable for practical agricultural deployment. The code is available at: https://www.github.com/sabermehdipour/MXiT.
Mehdipour et al. (Wed,) studied this question.