Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration
This addresses agricultural productivity losses due to plant diseases by providing a tool for farmers, but it is incremental as it applies existing CNN methods to a new dataset.
The study tackled plant disease detection by using convolutional neural networks on leaf images to diagnose 26 diseases across 14 plant classes, achieving 98.14% accuracy and integrating the model into a mobile app for real-time use.
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. We focus on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.