International Journal of Scientific Research and Engineering Development

This research paper introduces an intelligent system for early detection of plant leaf diseases, addressing one of the major challenges in modern agriculture. Crop diseases caused by pathogens such as fungi, bacteria, and viruses significantly reduce yield and quality, leading to economic losses for farmers worldwide. Timely identification of these diseases is essential to ensure sustainable agricultural practices and food security. Traditional methods of disease detection rely heavily on manual inspection, which is often labor-intensive, time-consuming, and prone to human error. To overcome these limitations, this study proposes an automated approach using computer vision and deep learning techniques. The system processes leaf images through preprocessing steps such as resizing, normalization, and noise reduction, followed by classification using a Convolutional Neural Network (CNN) model. The proposed model is trained on a dataset of healthy and diseased plant leaves to accurately identify various disease types. Experimental results demonstrate that the system achieves high accuracy and reliability in disease classification. The proposed solution is designed to be user-friendly, cost-efficient, and scalable, making it suitable for real-time deployment in agricultural environments. This work aims to support farmers and agricultural experts by providing a rapid and effective tool for crop health monitoring and decision-making.

Prof.Kamble S.A, Jagjeet Nimbalkar, Shruti Pol, Sampada Shinde, Vaishnavi Mate,"CropHealth: AI-Based Leaf Disease Detection Using CNN" International Journal of Scientific Research and Engineering Development, V9(3): Page(279-282) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.