Plant Disease Detection System using Convolution Neural Network
Author(s): 1. Kaustav Sanyal, 2. Sudipta Pramanik, 2. Koushik Kumar, 2. Dhriti Sundar Mahato, 2. J oydeep Modak
Authors Affiliations:
Department of BCA, Bengal Institute of Science and technology, Purulia, West Bengal, India
DOIs:10.2015/IJIRMF/202506021 | Paper ID: IJIRMF202506021Abstract: Plant diseases significantly impact agricultural productivity, leading to economic losses and food security challenges. Early and accurate detection of plant diseases is crucial for effective crop management. This project proposes a Convolutional Neural Network (CNN)-based approach for automated plant disease detection using leaf images. The system classifies diseases by analysing visual patterns in the images, providing farmers with a quick and reliable diagnostic tool. The model is trained on a dataset of labelled images of healthy and diseased plants, achieving high accuracy in disease identification. The results demonstrate the potential of deep learning in revolutionizing agricultural practices by enabling timely interventions.
Kaustav Sanyal, Sudipta Pramanik, Koushik Kumar, Dhriti Sundar Mahato, J oydeep Modak (2025); Plant Disease Detection System using Convolution Neural Network, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-6, Pp.152-161. Available on – https://www.ijirmf.com/