• Karpagavalli K
• Bishma JJR
• Dharshanaa KL
• Akshaya R

Millions of farmers worldwide are impacted by the frequent threat that rice leaf diseases provide to rice production. Early detection and treatment of rice leaf infection are critical for rice plants to grow healthy and produce enough food for the fast growing population. Vibrant visual background complicate computer-aided diagnosis of rice leaf disease. Convolutional neural networks (CNNs) use deep learning extensively to diagnose issues by identifying the issue through a user interface by applying features that are extracted from images. The seven known common diseases of rice leaves include sheath blight, brown spot, rice yellow mottle virus, bacterial leaf blight, leaf blast, leaf scald, and narrow brown spot.This research aims to improve automatic plant disease detection and analysis, providing solutions for early disease detection, disease prediction,and pesticide recommendations.By using CNN technology, it is possible to solve complex agricultural problems with an accurate and effective user interface and guarantee that rice production remains sustainable in the face of rising global food demands.

Convolutional Neural Network,Deep Learning, Artificial Intelligence, and Rice leaf.

"RICE LEAF DISEASES DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.f542-f547, March-2024, Available :http://www.jetir.org/papers/JETIR2403565.pdf

"RICE LEAF DISEASES DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppf542-f547, March-2024, Available at : http://www.jetir.org/papers/JETIR2403565.pdf