Abstract:
The research uses robust learning methods to identify powdery mildew and
leaf scorch on strawberry fruit and leaves. The suggested method combines
ResNet50 and InceptionV3 cognitive neural architectures. This method
involves collecting Powdery Mildew and Leaf Scorch datasets and improving
them. ResNet50 and InceptionV3 extract distinguishing characteristics and
identify powdery mildew and leaf scorch patterns. The combined attributes
are sent to a fully connected layer and a softmax classifier to help sort the
diseases. An optimised approach modifies the model’s hyperparameters
during training for optimal classification performance. Thorough evaluation
shows that the combined model outperforms ResNet50, InceptionV3, and
other disease identification methods. The merging of features using fusion
methods improves representation and disease detection. Research suggests
that the reinforcement learning-based approach can detect powdery mildew
and leaf scorch in strawberry fruits and leaves with an accuracy rate of
98.00% and a validation rate of 97.34%. This technology has an impact on
strawberry farming disease management.
