ABSTRACT
Pioneering method for detecting diseases in plant leaves by employing the architectural framework of Convolutional Neural Network (CNN) and leveraging Deep Learning techniques in the following paper. The approach detailed in this study showcases innovative strategies for accurately identifying various plant leaf diseases using advanced computational algorithms and neural network models. With agricultural losses due to such diseases in mind, we leverage a dataset of 87,867 images covering 38 classes of healthy and unhealthy leaves for robust model training. Our CNN model achieves an impressive 98.91% disease classification accuracy, aided by image preprocessing, segmentation, and feature extraction. Comparative analysis validates our approach's competitiveness, offering valuable insights for agricultural management through comprehensive disease identification and actionable insights for farmers.
