Automatic detection and classification of honey bee comb cells using deep learning
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/22321 |
Resumo: | In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource). |
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Automatic detection and classification of honey bee comb cells using deep learningApis mellifera L.Cell classificationDeep learningDeepBee softwareMachine learningSemantic segmentationIn a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).This research was developed in the framework of the project “BeeHope - Honeybee conservation centers in Western Europe: an innovative strategy using sustainable beekeeping to reduce honeybee decline”, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).Biblioteca Digital do IPBAlves, Thiago da SilvaPinto, M. AliceVentura, PauloNeves, Cátia J.Biron, David G.Candido Junior, ArnaldoPaula Filho, Pedro L. deRodrigues, Pedro João2018-01-19T10:00:00Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/22321engAlves, Thiago S.; Pinto, M. Alice; Ventura, Paulo; Neves, Cátia J.; Biron, David G.; Junior, Arnaldo C.; De Paula Filho, Pedro L.; Rodrigues, Pedro J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture. ISSN 0168-1699. 170, p. 1-140168-169910.1016/j.compag.2020.105244info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:50:03Zoai:bibliotecadigital.ipb.pt:10198/22321Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:13:34.262239Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic detection and classification of honey bee comb cells using deep learning |
title |
Automatic detection and classification of honey bee comb cells using deep learning |
spellingShingle |
Automatic detection and classification of honey bee comb cells using deep learning Alves, Thiago da Silva Apis mellifera L. Cell classification Deep learning DeepBee software Machine learning Semantic segmentation |
title_short |
Automatic detection and classification of honey bee comb cells using deep learning |
title_full |
Automatic detection and classification of honey bee comb cells using deep learning |
title_fullStr |
Automatic detection and classification of honey bee comb cells using deep learning |
title_full_unstemmed |
Automatic detection and classification of honey bee comb cells using deep learning |
title_sort |
Automatic detection and classification of honey bee comb cells using deep learning |
author |
Alves, Thiago da Silva |
author_facet |
Alves, Thiago da Silva Pinto, M. Alice Ventura, Paulo Neves, Cátia J. Biron, David G. Candido Junior, Arnaldo Paula Filho, Pedro L. de Rodrigues, Pedro João |
author_role |
author |
author2 |
Pinto, M. Alice Ventura, Paulo Neves, Cátia J. Biron, David G. Candido Junior, Arnaldo Paula Filho, Pedro L. de Rodrigues, Pedro João |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Alves, Thiago da Silva Pinto, M. Alice Ventura, Paulo Neves, Cátia J. Biron, David G. Candido Junior, Arnaldo Paula Filho, Pedro L. de Rodrigues, Pedro João |
dc.subject.por.fl_str_mv |
Apis mellifera L. Cell classification Deep learning DeepBee software Machine learning Semantic segmentation |
topic |
Apis mellifera L. Cell classification Deep learning DeepBee software Machine learning Semantic segmentation |
description |
In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource). |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-19T10:00:00Z 2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/22321 |
url |
http://hdl.handle.net/10198/22321 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Alves, Thiago S.; Pinto, M. Alice; Ventura, Paulo; Neves, Cátia J.; Biron, David G.; Junior, Arnaldo C.; De Paula Filho, Pedro L.; Rodrigues, Pedro J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture. ISSN 0168-1699. 170, p. 1-14 0168-1699 10.1016/j.compag.2020.105244 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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