Attention-Driven Dual Optimization Framework for Accurate and Generalizable Maize Leaf Disease Classification
The proposed Dual-Optimization Convolutional Neural Network to improve the accuracy and generalization of maize leaf disease classification achieved an accuracy of approximately 89%, outperforming the baseline CNN, PSO, and BO models, thus validating the effectiveness of the dual optimization framework.