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This is a Quantitative Study study.

Enhanced Plant Leaf Disease Detection Using CLAHE-Processed Images and Custom CNN Deep Learning Architecture

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This analysis demonstrates reliable disease classification in plant leaves using deep learning, highlighting its potential in precision agriculture.

The model achieved 93% test accuracy, indicating its capability in accurately identifying plant leaf diseases.
Using the Adam optimizer, the custom CNN architecture showed impressive training accuracy of 94.64% and validation accuracy of 92.95%.
Data augmentation techniques enhanced model generalization, ensuring quality training on the Plant Village dataset, which included healthy and diseased images.
Results validate the model's applicability for smart farming, especially in mobile and IoT-based real-time disease monitoring.

Neelam Sulaiya (2025) studied this question.

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This analysis demonstrates reliable disease classification in plant leaves using deep learning, highlighting its potential in precision agriculture.