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A comparative study on plant disease detection using machine learning algorithm.

The crop diseases are major problem in agriculture industry that requires an accurate and fast crop disease detection method to prevent and limiting major loss. Many researchers utilize machine learning algorithm to achieve this solution. Majority of the solution either using traditional machine lea...

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Main Authors: Mohd. Anuar, Mohd. Syahid, Kadir, Muhammad Solihin
Format: Article
Language:English
Published: Penerbit UTM Press 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/104606/1/MuhammadSolihinKadirSyahidAnuar2022_AcomparativeStudyonPlantDeseaseDetection.pdf
http://eprints.utm.my/104606/
https://oiji.utm.my/index.php/oiji/article/view/217
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spelling my.utm.1046062024-02-21T08:27:18Z http://eprints.utm.my/104606/ A comparative study on plant disease detection using machine learning algorithm. Mohd. Anuar, Mohd. Syahid Kadir, Muhammad Solihin T Technology (General) The crop diseases are major problem in agriculture industry that requires an accurate and fast crop disease detection method to prevent and limiting major loss. Many researchers utilize machine learning algorithm to achieve this solution. Majority of the solution either using traditional machine learning algorithm or deep learning-based algorithm. For traditional machine learning algorithm, the algorithm usually used feature extraction algorithm paired with machine learning algorithm such as Support Vector Machine, Logistic Regression and K-Neighbors. Deep learning-based algorithm utilize either fully connected neural network or use convolution neural network as feature extractor and paired it with machine learning classifier. However, evaluating those algorithms are quite difficult due to different settings in each experiment done in evaluating deep learning-based algorithm and traditional machine learning based algorithm. The purpose of this paper is to evaluate those algorithms with same dataset which is Plant Village dateset to give them fair comparison in performance. The results show that both machine learning and deep learning algorithm achieve great result with the highest accuracy achieve around 97% accuracy. Penerbit UTM Press 2022-12-15 Article PeerReviewed application/pdf en http://eprints.utm.my/104606/1/MuhammadSolihinKadirSyahidAnuar2022_AcomparativeStudyonPlantDeseaseDetection.pdf Mohd. Anuar, Mohd. Syahid and Kadir, Muhammad Solihin (2022) A comparative study on plant disease detection using machine learning algorithm. Open International Journal Of Informatics (OIJI), 10 (2). pp. 117-123. ISSN 2289-2370 https://oiji.utm.my/index.php/oiji/article/view/217
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mohd. Anuar, Mohd. Syahid
Kadir, Muhammad Solihin
A comparative study on plant disease detection using machine learning algorithm.
description The crop diseases are major problem in agriculture industry that requires an accurate and fast crop disease detection method to prevent and limiting major loss. Many researchers utilize machine learning algorithm to achieve this solution. Majority of the solution either using traditional machine learning algorithm or deep learning-based algorithm. For traditional machine learning algorithm, the algorithm usually used feature extraction algorithm paired with machine learning algorithm such as Support Vector Machine, Logistic Regression and K-Neighbors. Deep learning-based algorithm utilize either fully connected neural network or use convolution neural network as feature extractor and paired it with machine learning classifier. However, evaluating those algorithms are quite difficult due to different settings in each experiment done in evaluating deep learning-based algorithm and traditional machine learning based algorithm. The purpose of this paper is to evaluate those algorithms with same dataset which is Plant Village dateset to give them fair comparison in performance. The results show that both machine learning and deep learning algorithm achieve great result with the highest accuracy achieve around 97% accuracy.
format Article
author Mohd. Anuar, Mohd. Syahid
Kadir, Muhammad Solihin
author_facet Mohd. Anuar, Mohd. Syahid
Kadir, Muhammad Solihin
author_sort Mohd. Anuar, Mohd. Syahid
title A comparative study on plant disease detection using machine learning algorithm.
title_short A comparative study on plant disease detection using machine learning algorithm.
title_full A comparative study on plant disease detection using machine learning algorithm.
title_fullStr A comparative study on plant disease detection using machine learning algorithm.
title_full_unstemmed A comparative study on plant disease detection using machine learning algorithm.
title_sort comparative study on plant disease detection using machine learning algorithm.
publisher Penerbit UTM Press
publishDate 2022
url http://eprints.utm.my/104606/1/MuhammadSolihinKadirSyahidAnuar2022_AcomparativeStudyonPlantDeseaseDetection.pdf
http://eprints.utm.my/104606/
https://oiji.utm.my/index.php/oiji/article/view/217
_version_ 1792147867658354688
score 13.211869

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