August 13, 2025Open Access

Enhanced residual-attention deep neural network for disease classification in maize leaf images

Key Points

Maize Net achieved 0.9595 classification accuracy in identifying diseases in maize leaves, showing a strong performance.
The model was validated with a five-fold cross-validation and addressed class imbalance using macro-average evaluation metrics.
By incorporating an attention mechanism, the model improves efficiency by focusing on relevant features, enhancing accuracy.
Residual learning was used to tackle the vanishing gradient problem, confirming the model’s robustness in deep learning applications.

Abstract

Abstract Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model’s efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset’s class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet’s robustness and reliability in automated plant disease classification.

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