The study uses powerful learning methods to identify powdery mildew and leaf scorch on strawberry fruit and leaves. The suggested solution combines ResNet50 and InceptionV3 cognitive neural networks. This strategy involves collecting powdery mildew and leaf scorch datasets and improving them. ResNet50 and InceptionV3 extract discriminative features and detect powdery mildew and leaf scorch patterns. The aggregate characteristics are put into a fully connected layer and a softmax classifier for illness classification. An optimized technique adjusts the model’s hyperparameters during training for optimal classification performance. Comprehensive testing shows that the combined model beats ResNet50, InceptionV3, and other illness diagnostic approaches. Integration of information utilizing fusion approaches improves representation and illness diagnosis. Research suggests that the reinforcement learning-based approach can identify powdery mildew and leaf scorch in strawberry fruits with an accuracy rate of 98.42% and a validation rate of 99%. This technology impacts strawberry crop disease control.