Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification

Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at s...
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Autor*in:	Jacó C. Gomes [verfasserIn] Díbio L. Borges [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	few-shot learning insect pest classification insect maturity stages RGB images

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 12(2022), 8, p 1733
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:8, p 1733

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy12081733

Katalog-ID:	DOAJ079242367

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520			\|a Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains.
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spelling	10.3390/agronomy12081733 doi (DE-627)DOAJ079242367 (DE-599)DOAJ049b7ef727e948cd8e4073f2919ffb58 DE-627 ger DE-627 rakwb eng Jacó C. Gomes verfasserin aut Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains. few-shot learning insect pest classification insect maturity stages RGB images Agriculture S Díbio L. Borges verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 8, p 1733 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:8, p 1733 https://doi.org/10.3390/agronomy12081733 kostenfrei https://doaj.org/article/049b7ef727e948cd8e4073f2919ffb58 kostenfrei https://www.mdpi.com/2073-4395/12/8/1733 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 8, p 1733
allfields_unstemmed	10.3390/agronomy12081733 doi (DE-627)DOAJ079242367 (DE-599)DOAJ049b7ef727e948cd8e4073f2919ffb58 DE-627 ger DE-627 rakwb eng Jacó C. Gomes verfasserin aut Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains. few-shot learning insect pest classification insect maturity stages RGB images Agriculture S Díbio L. Borges verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 8, p 1733 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:8, p 1733 https://doi.org/10.3390/agronomy12081733 kostenfrei https://doaj.org/article/049b7ef727e948cd8e4073f2919ffb58 kostenfrei https://www.mdpi.com/2073-4395/12/8/1733 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 8, p 1733
allfieldsGer	10.3390/agronomy12081733 doi (DE-627)DOAJ079242367 (DE-599)DOAJ049b7ef727e948cd8e4073f2919ffb58 DE-627 ger DE-627 rakwb eng Jacó C. Gomes verfasserin aut Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains. few-shot learning insect pest classification insect maturity stages RGB images Agriculture S Díbio L. Borges verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 8, p 1733 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:8, p 1733 https://doi.org/10.3390/agronomy12081733 kostenfrei https://doaj.org/article/049b7ef727e948cd8e4073f2919ffb58 kostenfrei https://www.mdpi.com/2073-4395/12/8/1733 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 8, p 1733
allfieldsSound	10.3390/agronomy12081733 doi (DE-627)DOAJ079242367 (DE-599)DOAJ049b7ef727e948cd8e4073f2919ffb58 DE-627 ger DE-627 rakwb eng Jacó C. Gomes verfasserin aut Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains. few-shot learning insect pest classification insect maturity stages RGB images Agriculture S Díbio L. Borges verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 8, p 1733 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:8, p 1733 https://doi.org/10.3390/agronomy12081733 kostenfrei https://doaj.org/article/049b7ef727e948cd8e4073f2919ffb58 kostenfrei https://www.mdpi.com/2073-4395/12/8/1733 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 8, p 1733
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source	In Agronomy 12(2022), 8, p 1733 volume:12 year:2022 number:8, p 1733
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author	Jacó C. Gomes
spellingShingle	Jacó C. Gomes misc few-shot learning misc insect pest classification misc insect maturity stages misc RGB images misc Agriculture misc S Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification
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topic_title	Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification few-shot learning insect pest classification insect maturity stages RGB images
topic	misc few-shot learning misc insect pest classification misc insect maturity stages misc RGB images misc Agriculture misc S
topic_unstemmed	misc few-shot learning misc insect pest classification misc insect maturity stages misc RGB images misc Agriculture misc S
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title	Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification
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title_sort	insect pest image recognition: a few-shot machine learning approach including maturity stages classification
title_auth	Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification
abstract	Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains.
abstractGer	Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains.
abstract_unstemmed	Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totalling 6817 images. Second, a few-shot prototypical network is proposed based on a comparison with other state-of-art models and further divergence analysis. Experiments were conducted separating the adult classes and the early stages into different groups. The best results achieved an accuracy of 86.33% for the adults, and 87.91% for early stages, both using a Kullback–Leibler divergence measure. These results are promising regarding a crop scenario where the more significant pests are few and it is important to detect them at earlier stages. Further research directions would be in evaluating a similar approach in particular crop ecosystems, and testing cross-domains.
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title_short	Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification
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