Hybrid CNN–Transformer Model for Maize Leaf Blight Classification using Adaptive Genetic Optimization

Maize leaf blight is a disastrous foliar disease in the world production of maize that causes significant losses in terms of yield annually. The classical machine learning and convolutional neural network (CNN) models are prone to poor generalization across the different conditions in the field because of differences in lighting, background, and leaf morphology. To counter these difficulties, this research suggests the use of Hybrid CNN-Transformer architecture that is trained using the Adaptive Genetic Optimization (AGO) to provide accurate classification of maize leaf blight. The hybrid model will utilize the spatial feature extraction and Global attention mechanism of Vision Transformer (ViT) to extract local and contextual patterns of diseases. AGO algorithm is dynamically adjusted with essential hyperparameters, such as the learning rate, filter size, and embedding, and adjusted according to the population diversity and fitness assessment, thus enhancing the convergence speed and classification accuracy. The experimental analysis of an augmented dataset of maize leaf disease proves that the developed model has a higher classification accuracy of 98.1, compared to the base models, including ResNet50 (96.7) and MobileNetV3 (97.2). The hybrid AGOCNN Transformer version demonstrates better solutions to the intelligent system on agricultural disease management in the real-field conditions.