• Omkar Vitthal Chaudhari
• Anil Dayanand Gujar
• Sumit Shankar Chaskar
• Avinash Sudamrao Baikare
• Deepali Sale

Cotton cultivation is an important economic activity all over the world. However, it faces many challenges due to various pests and diseases that adversely affect the plant's leaf area. This project aims to develop a web application by using deep learning algorithms to identify and distinguish common cotton diseases, such as those caused by sucking and chewing pests, curl virus etc. The application will not only diagnose diseases but also recommend suitable solutions to farmers. Early disease detection is crucial for preserving crop yield. The proposed model, employing meta-deep learning and Convolutional Neural Networks (CNNs), promises accurate disease identification, contributing to the protection of cotton crops

Deep Learning, Convolutional Neural Network (CNN), cotton leaf disease, crop disease identification

"Cotton Disease Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a669-a674, November-2023, Available :http://www.jetir.org/papers/JETIR2311083.pdf

"Cotton Disease Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa669-a674, November-2023, Available at : http://www.jetir.org/papers/JETIR2311083.pdf