ArticleHeliyon2024
A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A customized MobileNetV2-based lightweight CNN for monkeypox detection and classification.Scientific reports · 2026Article
- CoNutriNet: a dual-branch architecture with DenseNet and graph-enhanced attention network for coffee nutrient deficiency classification.Frontiers in plant science · 2026Article
- Edge-enhanced dual branch CNN with adaptive attention for robust apple leaf disease detection.BMC plant biology · 2025Article
- A novel framework GRCornShot for corn disease detection using few shot learning with prototypical network.Scientific reports · 2025Article
- Article
- Mobile based deep CNN model for maize leaf disease detection and classification.Plant methods · 2025Article
- Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.Journal of orthopaedic surgery and research · 2025Article
- Article
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8 authors.
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Abstract
Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.
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