Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration

Abstract

Abstract 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.

Code and data availability

The paper's entire phenotyping analysis (CNN training/evaluation on 87,867 leaf images across 14 crops and 26 diseases) is based on a public Kaggle dataset explicitly cited by the authors with a URL, making it a public, paper-specific, actionable asset. No author analysis code, trained model checkpoints, or mobile app源