• Prathmesh Ghangare
• Dr. P.D. Khandait
• Pranay M Zingre
• Ashish Mahalle

Streamlit- enabled deep learning framework for identifying cotton and apple leaf illnesses, integrating Convolutional neural networks, or CNN's, are used to achieve accurate classification. The framework leverages Streamlit, a lightweight Python-based web application tool, to provide an interactive and user-friendly interface for illness detection in real time. The Deep learning model is trained on an extensive dataset of diseased and healthy leaf images, enabling precise identification of conditions such as leaf blight, rust, mildew, and bacterial infections. By enabling farmers and agricultural specialists to upload leaf photos and get immediate diagnostic feedback, the suggested solution improves accessibility. By providing a scalable, economical, and effective method for detecting plant diseases, this research seeks to advance precision agriculture and ultimately support crop protection and yield optimization.

Streamlit, Deep Learning, CNN, Cotton Leaf Disease, Apple Leaf Disease, Plant Disease Detection, Precision Agriculture, Web-based Diagnosis, Image Classification, Smart Farming

"STREAMLIT-ENABLED DEEP LEARNING FRAMEWORK FOR DETECTING COTTON AND APPLE LEAF DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h697-h705, March-2025, Available :http://www.jetir.org/papers/JETIR2503785.pdf

"STREAMLIT-ENABLED DEEP LEARNING FRAMEWORK FOR DETECTING COTTON AND APPLE LEAF DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph697-h705, March-2025, Available at : http://www.jetir.org/papers/JETIR2503785.pdf