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Review of Wheat Disease Classification and Severity Detection Models

Wheat is an important cereal crop that feeds more than a third of the world's population. The yield of wheat depends on various factors. Among them, disease is an important factor affecting the yield and quality of wheat. To combat these diseases, researchers have been studying the use of adva...

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Main Authors:	Hongyan, Zang, Annie, Joseph, Shourong, Zhang, Rong, Liu, Wanzhen, Wang
Format:	Article
Language:	English
Published:	ARQII Publication 2023
Subjects:	SB Plant culture TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TN Mining engineering. Metallurgy
Online Access:	http://ir.unimas.my/id/eprint/43741/3/Review%20of.pdf http://ir.unimas.my/id/eprint/43741/ https://www.sciencedirect.com/science/article/abs/pii/S0885576522001552 https://doi.org/10.1016/j.pmpp.2022.101940
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id	my.unimas.ir.43741
record_format	eprints
spelling	my.unimas.ir.437412023-12-18T02:18:18Z http://ir.unimas.my/id/eprint/43741/ Review of Wheat Disease Classification and Severity Detection Models Hongyan, Zang Annie, Joseph Shourong, Zhang Rong, Liu Wanzhen, Wang SB Plant culture TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TN Mining engineering. Metallurgy Wheat is an important cereal crop that feeds more than a third of the world's population. The yield of wheat depends on various factors. Among them, disease is an important factor affecting the yield and quality of wheat. To combat these diseases, researchers have been studying the use of advanced techniques such as deep plant disease learning and image processing methods for identification. In the current study, there are many researches for wheat disease classification, but less for wheat disease severity recognition or estimate. The existing wheat disease severity detection is basically achieved by classification. Moreover, the same disease shows different symptoms at different periods or at different degrees of infection, which increases the difficulty of disease identification. In order to fully grasp the core technology of wheat disease recognition, this paper reviews the research of deep learning technology in wheat leaf disease classification and wheat disease severity. Special attention is paid to the application of image segmentation technology in wheat disease severity recognition. This paper mainly aims to explain deep learning-based wheat diseases identification algorithm, and to discuss the benefits and drawbacks of present wheat disease detection approaches. The main conclusion is that the classification of wheat diseases and the severity of wheat diseases have made good progress, but they are still in the state of independent research. Hybrid algorithm is a new way and a new challenge to link the two tasks. ARQII Publication 2023-12 Article PeerReviewed text en http://ir.unimas.my/id/eprint/43741/3/Review%20of.pdf Hongyan, Zang and Annie, Joseph and Shourong, Zhang and Rong, Liu and Wanzhen, Wang (2023) Review of Wheat Disease Classification and Severity Detection Models. Application of Modelling and Simulation, 7 (2023). pp. 201-213. ISSN 2600-8084 https://www.sciencedirect.com/science/article/abs/pii/S0885576522001552 https://doi.org/10.1016/j.pmpp.2022.101940
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English
topic	SB Plant culture TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TN Mining engineering. Metallurgy
spellingShingle	SB Plant culture TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TN Mining engineering. Metallurgy Hongyan, Zang Annie, Joseph Shourong, Zhang Rong, Liu Wanzhen, Wang Review of Wheat Disease Classification and Severity Detection Models
description	Wheat is an important cereal crop that feeds more than a third of the world's population. The yield of wheat depends on various factors. Among them, disease is an important factor affecting the yield and quality of wheat. To combat these diseases, researchers have been studying the use of advanced techniques such as deep plant disease learning and image processing methods for identification. In the current study, there are many researches for wheat disease classification, but less for wheat disease severity recognition or estimate. The existing wheat disease severity detection is basically achieved by classification. Moreover, the same disease shows different symptoms at different periods or at different degrees of infection, which increases the difficulty of disease identification. In order to fully grasp the core technology of wheat disease recognition, this paper reviews the research of deep learning technology in wheat leaf disease classification and wheat disease severity. Special attention is paid to the application of image segmentation technology in wheat disease severity recognition. This paper mainly aims to explain deep learning-based wheat diseases identification algorithm, and to discuss the benefits and drawbacks of present wheat disease detection approaches. The main conclusion is that the classification of wheat diseases and the severity of wheat diseases have made good progress, but they are still in the state of independent research. Hybrid algorithm is a new way and a new challenge to link the two tasks.
format	Article
author	Hongyan, Zang Annie, Joseph Shourong, Zhang Rong, Liu Wanzhen, Wang
author_facet	Hongyan, Zang Annie, Joseph Shourong, Zhang Rong, Liu Wanzhen, Wang
author_sort	Hongyan, Zang
title	Review of Wheat Disease Classification and Severity Detection Models
title_short	Review of Wheat Disease Classification and Severity Detection Models
title_full	Review of Wheat Disease Classification and Severity Detection Models
title_fullStr	Review of Wheat Disease Classification and Severity Detection Models
title_full_unstemmed	Review of Wheat Disease Classification and Severity Detection Models
title_sort	review of wheat disease classification and severity detection models
publisher	ARQII Publication
publishDate	2023
url	http://ir.unimas.my/id/eprint/43741/3/Review%20of.pdf http://ir.unimas.my/id/eprint/43741/ https://www.sciencedirect.com/science/article/abs/pii/S0885576522001552 https://doi.org/10.1016/j.pmpp.2022.101940
_version_	1787140535750754304
score	13.211869

Review of Wheat Disease Classification and Severity Detection Models

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