• Deepthi Garikimukkala
• Venkata Durgarao Matta

With the growing need to boost agricultural productivity, innovative technologies are becoming increasingly vital in helping farmers identify and classify plant leaf diseases at an early stage. Advanced imaging systems offer a promising approach, enabling the rapid and accurate detection of diseases affecting plant leaves. Early intervention is crucial, as untreated diseases can severely diminish both the yield and quality of crops. Various types of leaf diseases frequently emerge in agricultural settings, posing significant risks to crop health.This paper examines the use of image processing techniques, including segmentation, feature extraction, and classification, as effective, dependable, and precise methods for detecting and categorizing plant leaf diseases. It provides a comprehensive overview of current research, focusing on key processes such as image acquisition, pre-processing, segmentation, feature extraction, and classification. By consolidating findings from multiple studies, this review aims to equip farmers with effective tools to enhance disease management strategies.Additionally, the review highlights the revolutionary potential of deep learning approaches in improving disease detection and classification, empowering farmers to make informed decisions and mitigate the adverse effects of plant leaf diseases on crop production.

Enhancing Agriculture: Deep Learning for Plant Leaf Disease Classification

"Enhancing Agriculture: Deep Learning for Plant Leaf Disease Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.e10-e16, November-2024, Available :http://www.jetir.org/papers/JETIR2411402.pdf

"Enhancing Agriculture: Deep Learning for Plant Leaf Disease Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppe10-e16, November-2024, Available at : http://www.jetir.org/papers/JETIR2411402.pdf