CapPlant: a capsule network based framework for plant disease classification

Accurate disease classification in plants is important for a profound understanding of their growth and health. Recognizing diseases in plants from images is one of the critical and challenging problem in agriculture. In this research, a deep learning architecture model (CapPlant) is proposed that u...

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Autores principales:	Omar Bin Samin, Maryam Omar, Musadaq Mansoor
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Convolutional neural network Plant disease classification Deep learning Capsule network. Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/1643f676f9cb46e5a2a38f576aeb8bd1
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spelling	oai:doaj.org-article:1643f676f9cb46e5a2a38f576aeb8bd12021-11-07T15:05:13ZCapPlant: a capsule network based framework for plant disease classification10.7717/peerj-cs.7522376-5992https://doaj.org/article/1643f676f9cb46e5a2a38f576aeb8bd12021-11-01T00:00:00Zhttps://peerj.com/articles/cs-752.pdfhttps://peerj.com/articles/cs-752/https://doaj.org/toc/2376-5992Accurate disease classification in plants is important for a profound understanding of their growth and health. Recognizing diseases in plants from images is one of the critical and challenging problem in agriculture. In this research, a deep learning architecture model (CapPlant) is proposed that utilizes plant images to predict whether it is healthy or contain some disease. The prediction process does not require handcrafted features; rather, the representations are automatically extracted from input data sequence by architecture. Several convolutional layers are applied to extract and classify features accordingly. The last convolutional layer in CapPlant is replaced by state-of-the-art capsule layer to incorporate orientational and relative spatial relationship between different entities of a plant in an image to predict diseases more precisely. The proposed architecture is tested on the PlantVillage dataset, which contains more than 50,000 images of infected and healthy plants. Significant improvements in terms of prediction accuracy has been observed using the CapPlant model when compared with other plant disease classification models. The experimental results on the developed model have achieved an overall test accuracy of 93.01%, with F1 score of 93.07%.Omar Bin SaminMaryam OmarMusadaq MansoorPeerJ Inc.articleConvolutional neural networkPlant disease classificationDeep learningCapsule network.Electronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e752 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolutional neural network Plant disease classification Deep learning Capsule network. Electronic computers. Computer science QA75.5-76.95
spellingShingle	Convolutional neural network Plant disease classification Deep learning Capsule network. Electronic computers. Computer science QA75.5-76.95 Omar Bin Samin Maryam Omar Musadaq Mansoor CapPlant: a capsule network based framework for plant disease classification
description	Accurate disease classification in plants is important for a profound understanding of their growth and health. Recognizing diseases in plants from images is one of the critical and challenging problem in agriculture. In this research, a deep learning architecture model (CapPlant) is proposed that utilizes plant images to predict whether it is healthy or contain some disease. The prediction process does not require handcrafted features; rather, the representations are automatically extracted from input data sequence by architecture. Several convolutional layers are applied to extract and classify features accordingly. The last convolutional layer in CapPlant is replaced by state-of-the-art capsule layer to incorporate orientational and relative spatial relationship between different entities of a plant in an image to predict diseases more precisely. The proposed architecture is tested on the PlantVillage dataset, which contains more than 50,000 images of infected and healthy plants. Significant improvements in terms of prediction accuracy has been observed using the CapPlant model when compared with other plant disease classification models. The experimental results on the developed model have achieved an overall test accuracy of 93.01%, with F1 score of 93.07%.
format	article
author	Omar Bin Samin Maryam Omar Musadaq Mansoor
author_facet	Omar Bin Samin Maryam Omar Musadaq Mansoor
author_sort	Omar Bin Samin
title	CapPlant: a capsule network based framework for plant disease classification
title_short	CapPlant: a capsule network based framework for plant disease classification
title_full	CapPlant: a capsule network based framework for plant disease classification
title_fullStr	CapPlant: a capsule network based framework for plant disease classification
title_full_unstemmed	CapPlant: a capsule network based framework for plant disease classification
title_sort	capplant: a capsule network based framework for plant disease classification
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/1643f676f9cb46e5a2a38f576aeb8bd1
work_keys_str_mv	AT omarbinsamin capplantacapsulenetworkbasedframeworkforplantdiseaseclassification AT maryamomar capplantacapsulenetworkbasedframeworkforplantdiseaseclassification AT musadaqmansoor capplantacapsulenetworkbasedframeworkforplantdiseaseclassification
_version_	1718443259120844800

CapPlant: a capsule network based framework for plant disease classification

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