• Chiranjeevi MP
• Darshan Simpi
• M J Abhishek
• Shivanand LP
• Prof.Aravind Naik

Plant health plays crucial role in assessing crop productivity levels in India often leading to significant losses in yield when adversely affected this study explores the use of a pre-trained VGG16 model to identify plant diseases and their symptoms by conducting simulations and experiments on sample datasets the CNN-based systems the model's effectiveness was assessed across various levels of complexity and regions impacted by plant diseases the system was improved iteratively to enhance both its accuracy and reliability the research focuses on 37 different types of diseased plant leaves including issues such as apple scab corn maize common rust and tomato yellow leaf curl infections assessing the impact of these diseases on diverse crops the proposed model achieved an impressive testing accuracy of 9647 several performance metrics were calculated such as CNN accuracy image processing indicators and evaluations across training and validation datasets this work demonstrates the powerful potential of pre- trained deep learning architectures such as VGG16 for detecting plant diseases leveraging artificial intelligence offers a practical solution to minimize the damaging effects of crop diseases helping secure agricultural output strengthen food security integrating such advanced technologies is critical promoting sustainable farming practices ensuring continuous growth and stability of India’s agriculture sector a vital contributor to the nations economy

AI BASED PLANT DISEASE PREDICTION AND DIAGNOSIS

"AI BASED PLANT DISEASE PREDICTION AND DIAGNOSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.13-18, July-2025, Available :http://www.jetir.org/papers/JETIRGX06004.pdf

"AI BASED PLANT DISEASE PREDICTION AND DIAGNOSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp13-18, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06004.pdf