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Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison...

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Main Authors: Muhammad Abdu, Aliyu, Mohd. Mokji, Musa, Ullah Sheikh, Usman
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/90900/1/MusaMohdMokji2020_MachineLearningforPlantDiseaseDetection.pdf
http://eprints.utm.my/id/eprint/90900/
http://dx.doi.org/10.11591/ijai.v9.i4.pp670-683
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spelling my.utm.909002021-05-31T13:28:33Z http://eprints.utm.my/id/eprint/90900/ Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning Muhammad Abdu, Aliyu Mohd. Mokji, Musa Ullah Sheikh, Usman TK Electrical engineering. Electronics Nuclear engineering Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security. Institute of Advanced Engineering and Science 2020-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90900/1/MusaMohdMokji2020_MachineLearningforPlantDiseaseDetection.pdf Muhammad Abdu, Aliyu and Mohd. Mokji, Musa and Ullah Sheikh, Usman (2020) Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning. IAES International Journal of Artificial Intelligence, 9 (4). pp. 670-683. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v9.i4.pp670-683
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 TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Muhammad Abdu, Aliyu
Mohd. Mokji, Musa
Ullah Sheikh, Usman
Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
description Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security.
format Article
author Muhammad Abdu, Aliyu
Mohd. Mokji, Musa
Ullah Sheikh, Usman
author_facet Muhammad Abdu, Aliyu
Mohd. Mokji, Musa
Ullah Sheikh, Usman
author_sort Muhammad Abdu, Aliyu
title Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
title_short Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
title_full Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
title_fullStr Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
title_full_unstemmed Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
title_sort machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://eprints.utm.my/id/eprint/90900/1/MusaMohdMokji2020_MachineLearningforPlantDiseaseDetection.pdf
http://eprints.utm.my/id/eprint/90900/
http://dx.doi.org/10.11591/ijai.v9.i4.pp670-683
_version_ 1702169617664835584
score 13.211869

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