TELKOMNIKA Telecommunication, Computing, Electronics and Control
Image based anthracnose and red-rust leaf disease detection using deep learning
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Image based anthracnose and red-rust leaf disease detection using deep learning
Image based anthracnose and red-rust leaf disease detection using deep learning
Subject
Convolutional networks, Deep neural network, Image classification, Mango leaf diseases,Transfer learning
Description
Deep residual learning frameworks have achieved great success in image
classification. This article presents the use of transfer learning which is
applied on mango leaf image dataset for its disease’s detection. New
methodology and training have been used to facilitate the easy and rapid
implementation of the mango leaf disease detection system in practice.
Proposed system can be used to identify the mango leaf for whether it is
healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
classification. This article presents the use of transfer learning which is
applied on mango leaf image dataset for its disease’s detection. New
methodology and training have been used to facilitate the easy and rapid
implementation of the mango leaf disease detection system in practice.
Proposed system can be used to identify the mango leaf for whether it is
healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Creator
Rajashree Y. Patil, Sampada Gulavani, Vishal B. Waghmare, Irfan K. Mujawar
Source
DOI: 10.12928/TELKOMNIKA.v20i6.24262
Publisher
Universitas Ahmad Dahlan
Date
December 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Rajashree Y. Patil, Sampada Gulavani, Vishal B. Waghmare, Irfan K. Mujawar, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Image based anthracnose and red-rust leaf disease detection using deep learning,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/4484.
Image based anthracnose and red-rust leaf disease detection using deep learning,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/4484.