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

TL;DR

This paper presents a CNN-based method for multi-class plant leaf disease detection, achieving high accuracy and integrating the model into a mobile app for real-time diagnosis across 14 plant classes and 26 diseases.

Contribution

It introduces a deep learning approach combined with mobile app integration for accurate, real-time plant disease detection across multiple crop types.

Findings

Achieved 98.14% accuracy in disease diagnosis

Successfully integrated the model into a mobile app for real-time use

Detected 26 diseases across 14 plant classes

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.