Greenguard: CNN-based system for Intelligent Plant Disease Classification

Plant diseases significantly impact agricultural output, yet traditional detection methods are costly, time-consuming, and inaccurate. This study proposes a more efficient disease recognition model based on image processing and deep learning, specifically Convolutional Neural Networks (CNNs). These networks perform well at analyzing leaf images to identify plant diseases. However, existing models trained on ideal conditions struggle with real-world field photos featuring complex backgrounds and multiple leaves. To address this challenge, the study will test and compare various CNN architectures like VGG19, AlexNet, and ResNet aiming to find the model with the highest accuracy in detecting diseases under realistic conditions. The ultimate goal is to develop a tool that empowers farmers to quickly and accurately identify crop diseases, ultimately leading to improved agricultural output and reduced losses.