Journal Article

Corn or maize leaf disease classification based on CNN and vision mamba model

Xu, Heng

2026

Overview

Accurate classification of corn leaf diseases is critical for timely detection and control of pests and diseases. By accurately recognizing different types of leaf diseases, farmers and agricultural experts can quickly take targeted control measures to reduce crop losses and safeguard corn yield and quality. Since these corn leaf disease images usually contain complex backgrounds, similar lesion features, and limited labeling data, it causes traditional convolutional neural networks (CNNs) to easily confuse the lesion region with the background, making it difficult to distinguish between different disease types. To address these limitations, we propose G-ResNet, a hybrid CNN-Vision Mamba network that enhances disease-relevant feature learning through a hierarchical feature attention module and a scale feature attention module. It was demonstrated experimentally that G-ResNet can better classify maize leaf disease images. The code is available at https://github.com/gustafmy/g_resnet.git .

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114 / 631/449 / 639/705 / Accuracy / Agricultural production / Classification / Classification Algorithms / Convolutional Neural Network / Convolutional Neural Networks / Corn / Corn or Maize Leaf Disease / Crop yield / Deep learning / Disease / G-ResNet / Humanities and Social Sciences / Image Processing, Computer-Assisted - methods / Leaves / multidisciplinary / Neural networks / Neural Networks, Computer / Pest control / Plant Diseases - classification / Plant Leaves - microbiology / Science / Science (multidisciplinary) / Semantics / Vegetables / Vision Mamba / Zea mays - microbiology