A Software Driven Crop Disease Detection and ...

A Software Driven Crop Disease Detection and Recommendation System

Author Name : Mrs. Rajeshwari G L, Divya M Nagavand, Gagana S, Hannah Susan Blesson, Keerthi M

DOI: https://doi.org/10.56025/IJARESM.2025.13072555

ABSTRACT With agriculture evolving through the integration of technology, conventional approaches to disease diagnosis are rapidly giving way to intelligent, automated systems. This research presents a software centric solution for detecting plant diseases using deep learning and real-time communication technologies. The proposed system utilizes Convolutional Neural Networks (CNNs) to classify crop diseases from images submitted by users via a Telegram bot interface. Trained on a robust dataset covering major crops paddy, tomato, cotton, and banana—the CNN model accurately identifies 19 distinct disease types. The backend, built using Python, TensorFlow, OpenCV, and Flask, ensures reliable image classification through preprocessing methods like resizing, grayscale conversion, and normalization. Following classification, the system instantly delivers crop-specific remedy suggestions, aiming to minimize reliance on manual diagnosis and external guidance