Methods of insect image capture and classification: A Systematic literature review

Insects are the largest, most diverse organism class. Their key role in many ecosystems means that it is important they are identified correctly for effective management. However, insect species identification is challenging and labour-intensive. This has prompted increasing interest in image-based...

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Autores principales:	Don Chathurika Kshanthi Amarathunga, John Grundy, Hazel Parry, Alan Dorin
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Image classification Machine learning Insect identification Pest monitoring Systematic literature review Agriculture (General) S1-972 Agricultural industries HD9000-9495
Acceso en línea:	https://doaj.org/article/0ecb5ac4680f4c3781aa37f04a7ae989
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spelling	oai:doaj.org-article:0ecb5ac4680f4c3781aa37f04a7ae9892021-11-20T05:16:21ZMethods of insect image capture and classification: A Systematic literature review2772-375510.1016/j.atech.2021.100023https://doaj.org/article/0ecb5ac4680f4c3781aa37f04a7ae9892021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S277237552100023Xhttps://doaj.org/toc/2772-3755Insects are the largest, most diverse organism class. Their key role in many ecosystems means that it is important they are identified correctly for effective management. However, insect species identification is challenging and labour-intensive. This has prompted increasing interest in image-based systems for rapid, reliable identification supported by advances in deep learning, computer vision, and sensing technologies. We conducted a systematic literature review (SLR) to analyse and compare primary studies of image-based insect detection and species classification methods. We initially identified 980 studies published between 2010–2020 and selected from these 69 relevant studies using explicitly defined inclusion/exclusion criteria. In this SLR, we conducted a detailed analysis of the primary studies’ dataset properties (i.e. insect species targeted, crops, geographical locations, image capture methods) and insect classification techniques. We provide recommendations for future research based on the gaps our survey identified. We found many studies were conducted in China, the USA, and Brazil, but none in the African continent. The majority of the studies (78.3%) aimed to identify crop pests, mainly of rice and wheat. Only three studies specifically targeted beneficial insects, bee species and predatory species. Insect species targeted by the studies were centred around 10 insect orders out of 28. The analysis of classification methods shows a recent trend toward applying deep learning techniques compared to shallow learning techniques for insect identification. The SLR provides insight into the current state of the art and indicates promising future directions for image-based insect identification and species classification relevant to Computer Science, Agriculture and Ecology research.Don Chathurika Kshanthi AmarathungaJohn GrundyHazel ParryAlan DorinElsevierarticleImage classificationMachine learningInsect identificationPest monitoringSystematic literature reviewAgriculture (General)S1-972Agricultural industriesHD9000-9495ENSmart Agricultural Technology, Vol 1, Iss , Pp 100023- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Image classification Machine learning Insect identification Pest monitoring Systematic literature review Agriculture (General) S1-972 Agricultural industries HD9000-9495
spellingShingle	Image classification Machine learning Insect identification Pest monitoring Systematic literature review Agriculture (General) S1-972 Agricultural industries HD9000-9495 Don Chathurika Kshanthi Amarathunga John Grundy Hazel Parry Alan Dorin Methods of insect image capture and classification: A Systematic literature review
description	Insects are the largest, most diverse organism class. Their key role in many ecosystems means that it is important they are identified correctly for effective management. However, insect species identification is challenging and labour-intensive. This has prompted increasing interest in image-based systems for rapid, reliable identification supported by advances in deep learning, computer vision, and sensing technologies. We conducted a systematic literature review (SLR) to analyse and compare primary studies of image-based insect detection and species classification methods. We initially identified 980 studies published between 2010–2020 and selected from these 69 relevant studies using explicitly defined inclusion/exclusion criteria. In this SLR, we conducted a detailed analysis of the primary studies’ dataset properties (i.e. insect species targeted, crops, geographical locations, image capture methods) and insect classification techniques. We provide recommendations for future research based on the gaps our survey identified. We found many studies were conducted in China, the USA, and Brazil, but none in the African continent. The majority of the studies (78.3%) aimed to identify crop pests, mainly of rice and wheat. Only three studies specifically targeted beneficial insects, bee species and predatory species. Insect species targeted by the studies were centred around 10 insect orders out of 28. The analysis of classification methods shows a recent trend toward applying deep learning techniques compared to shallow learning techniques for insect identification. The SLR provides insight into the current state of the art and indicates promising future directions for image-based insect identification and species classification relevant to Computer Science, Agriculture and Ecology research.
format	article
author	Don Chathurika Kshanthi Amarathunga John Grundy Hazel Parry Alan Dorin
author_facet	Don Chathurika Kshanthi Amarathunga John Grundy Hazel Parry Alan Dorin
author_sort	Don Chathurika Kshanthi Amarathunga
title	Methods of insect image capture and classification: A Systematic literature review
title_short	Methods of insect image capture and classification: A Systematic literature review
title_full	Methods of insect image capture and classification: A Systematic literature review
title_fullStr	Methods of insect image capture and classification: A Systematic literature review
title_full_unstemmed	Methods of insect image capture and classification: A Systematic literature review
title_sort	methods of insect image capture and classification: a systematic literature review
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0ecb5ac4680f4c3781aa37f04a7ae989
work_keys_str_mv	AT donchathurikakshanthiamarathunga methodsofinsectimagecaptureandclassificationasystematicliteraturereview AT johngrundy methodsofinsectimagecaptureandclassificationasystematicliteraturereview AT hazelparry methodsofinsectimagecaptureandclassificationasystematicliteraturereview AT alandorin methodsofinsectimagecaptureandclassificationasystematicliteraturereview
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Methods of insect image capture and classification: A Systematic literature review

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