D Siva Sankar Reddy , S Mahammada , R Jyothika , P Deekshitha , K Gowthami 1 4 (October-December) Our project presents an innovative approach to address the crucial issue of plant disease identification through the utilization of deep learning techniques Agricultural productivity is significantly affected by plant diseases leading to economic losses and food security concerns In this research a comprehensive dataset comprising images of healthy and diseased plants is collected and preprocessed for training deep convolutional neural networks CNNs The proposed system harnesses the power of deep learning to automatically learn intricate patterns and features from plant images enabling accurate classification between healthy and diseased states The trained model is evaluated on an independent dataset to assess its classification performance Various deep learning architectures such as VGG ResNet and Inception are experimented with to identify the most suitable architecture for achieving high accuracy and robustness To enhance the models generalization capability data augmentation techniques are employed during training Transfer learning is also explored allowing the pretrained models to be finetuned for the specific task of plant disease classification The performance metrics including accuracy precision recall and F1score are thoroughly evaluated to quantify the models effectiveness The proposed deep learningbased plant disease classification system holds great promise for realworld agricultural applications Its automated and accurate nature has the potential to revolutionize plant disease management by enabling early detection and timely intervention As a result this research contributes to the advancement of precision agriculture practices helping to mitigate the adverse impacts of plant diseases and promote sustainable agricultural production Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026