International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This project presents an automated plant disease detection system developed using Convolutional Neural Networks (CNNs) to support early and accurate diagnosis of crop diseases. Plant diseases significantly impact global agricultural productivity, and traditional manual inspection methods are often slow, inconsistent, and dependent on expert knowledge. To address these challenges, the proposed system leverages deep learning to classify diseases from leaf images with improved precision and reliability. A large dataset consisting of over 87,000 healthy and diseased leaf images across 38 classes was preprocessed and used to train a custom CNN model. The model effectively extracts spatial features from input images and achieves high performance, recording approximately 99% training accuracy and 97% validation accuracy. The solution is deployed as an interactive web application built with Streamlit, enabling users—particularly farmers and agronomists—to upload leaf images and receive real-time disease predictions. By offering a fast, affordable, and scalable diagnostic tool, this work contributes to smarter agricultural practices, timely disease management, reduced dependency on expert intervention, and overall enhancement of crop health monitoring. The study also highlights the potential of CNN-based systems to transform traditional plant disease diagnosis through efficient, user-friendly, and technology-driven approaches.

Keywords: Plant Disease Detection, Convolutional Neural Networks (CNNs), Deep Learning, Image Processing, Machine Learning, Feature Extraction, Automated Diagnosis, Agriculture Technology, Leaf Image Classification, Training and Validation, Dataset Preparation, Image Preprocessing, Transfer Learning, Streamlit Web Application, TensorFlow/Keras, Real-Time Prediction, Mobile/Field Deployment, Accuracy and Performance Metrics, Sustainable Agriculture, Precision Agriculture, Early Disease Detection, Computer Vision, Data Augmentation, Plant Health Monitoring, Model Evaluation, Classification Models, Disease Recognition System, Web-Based Interface, Model Optimization, Field Images, PlantVillage Dataset, Hyperspectral Imaging, Few-Shot Learning (FSL), Generative Adversarial Networks (GANs), Image Segmentation, Object Detection, Decision Support Systems (DSS).

Downloads: | DOI: 10.17148/IJARCCE.2025.141224