CropGuard: A Mobile-Based Plant Health Detection System Using Convolutional Neural Networks

Agriculture plays a vital role in ensuring food security and supporting the global economy. However, plant diseases remain one of the major challenges that significantly affect crop productivity and quality. Early detection of plant diseases is essential to prevent large-scale damage and to help farmers take timely preventive actions. Traditional methods of disease detection rely on manual inspection by agricultural experts, which can be time- consuming, expensive, and sometimes inaccurate. With the advancement of artificial intelligence and deep learning technologies, automated plant disease detection systems have become increasingly effective. This research presents CropGuard, a mobile-based plant health detection system that uses Convolutional Neural Networks (CNN) to identify plant diseases from leaf images. The proposed system allows farmers to capture images of plant leaves using a smartphone camera. The captured image is processed using image preprocessing techniques such as resizing and normalization before being analysed by a trained CNN model. The model extracts important visual features from the leaf image and classifies it as healthy or diseased based on patterns learned during the training phase. The system then displays the predicted disease information through the mobile application interface, allowing farmers to quickly understand the health condition of their crops. The dataset used for training consists of labelled images of healthy and diseased plant leaves, enabling the CNN model to achieve reliable performance. The proposed system demonstrates how artificial intelligence and mobile technology can be combined to provide a fast, accurate, and user-friendly solution for plant disease detection and smart agricultural practices.