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

by Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 41
Year of Publication: 2024
Authors: Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
10.5120/ijca2024924026

Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque . Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration. International Journal of Computer Applications. 186, 41 ( Sep 2024), 62-68. DOI=10.5120/ijca2024924026

@article{ 10.5120/ijca2024924026,
author = { Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque },
title = { Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 41 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 62-68 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number41/multi-class-plant-leaf-disease-detection-a-cnnbased-approach-with-mobile-app-integration/ },
doi = { 10.5120/ijca2024924026 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-30T23:02:32.022563+05:30
%A Md Aziz Hosen Foysal
%A Foyez Ahmed
%A Md Zahurul Haque
%T Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 41
%P 62-68
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
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. This study focuses 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Convolutional Neural Network Leaf Classification Disease Diagnosis Mobile Technologies

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