• Akshat Chandrapatle
• Dnyaneshwar Kanade
• Om Chavan
• Tushar Bhalchim
• Pritam Bhamare

With the increase in the global population, there is a growing demand for food. However, plant diseases can cause significant financial losses for farmers and constitute a threat to agricultural productivity. Hence, the timely identification of plant diseases plays a critical role in ensuring food security and minimizing losses in agricultural production. Therefore, early detection and diagnosis of plant diseases are of utmost importance. Monitoring the health of plants and detecting diseases is vital for achieving sustainable agriculture In this study, we suggest a CNN model for identifying plant diseases using image processing. The development and technical implementation of the model is discussed in detail. Our approach represents a promising solution for the before-detection of plant diseases, which can help to ensure sustainable agriculture and food security. The suggested model has the potential to enhance the precision of disease detection and reduce the duration required for diagnosis. This can lead to faster treatment and prevention of the spread of diseases, resulting in increased crop yield and reduced losses for farmers.

Convolutional Neural Networking, Detection, Disease, Image Processing, Sustainable

"Plant Disease Detection using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.g282-g286, May-2023, Available :http://www.jetir.org/papers/JETIR2305640.pdf

"Plant Disease Detection using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppg282-g286, May-2023, Available at : http://www.jetir.org/papers/JETIR2305640.pdf