• A.VENKA REDDY
• R.KEERTHANA
• R.VIVEKANANDA
• R.PRUDHVI
• R.SURENDRA REDDY

: The paper presents the early detection of plant leaf diseases is critical for ensuring crop health and maximizing agricultural productivity. Traditional disease identification methods are often manual, time-consuming, and reliant on expert knowledge, which limits their scalability. In this study, we propose a deep learning-based framework employing Convolutional Neural Networks (CNNs) for the automatic detection and classification of leaf diseases from digital images. Our CNN architecture effectively extracts hierarchical features and classifies diseases with high accuracy. Experimental results demonstrate that the proposed model achieves an overall classification accuracy of over 95%, outperforming traditional machine learning approaches. The proposed system offers a scalable, fast, and cost-effective solution for real-time plant disease monitoring, which can aid farmers and agricultural experts in precision farming practices.

"Journal of Emerging Technologies and Innovative Research", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b146-b149, May-2025, Available :http://www.jetir.org/papers/JETIR2505117.pdf

"Journal of Emerging Technologies and Innovative Research", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb146-b149, May-2025, Available at : http://www.jetir.org/papers/JETIR2505117.pdf