Publicação
Deep learning recognition of a large number of pollen grain types
| Resumo: | Pollen in honey reflects its botanical origin and melissopalynology is used to identify origin, type and quantities of pollen grains of the botanical species visited by bees. Automatic pollen counting and classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. Despite the efforts made during the last decades, the manual classification process is still predominant. One of the reasons for that is the small number of types usually used in previous studies. In this paper, we present a large study to automatically identify pollen grains using nine state-of-the-art CNN techniques applied to the recently published POLEN73S image dataset. We observe that existing published approaches used original images without study the possible biased recognition due to pollen’s background colour or using preprocessing techniques. Our proposal manages to classify up to 97.4 % of the samples from the dataset with 73 different types of pollen. This result, which surpasses previous attempts in number and difficulty of pollen types under consideration, is an important step towards fully automatic pollen recognition, even with a large number of pollen grain types. |
|---|---|
| Autores principais: | Monteiro, Fernando C. |
| Outros Autores: | Pinto, Cristina M.; Rufino, José |
| Assunto: | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172805228888064 |
|---|---|
| author | Monteiro, Fernando C. |
| author2 | Pinto, Cristina M. Rufino, José |
| author2_role | author author |
| author_facet | Monteiro, Fernando C. Pinto, Cristina M. Rufino, José |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Monteiro, Fernando C.\",\"Person.identifier.orcid\":\"0000-0002-1421-8006\"},{\"Person.name\":\"Pinto, Cristina M.\"},{\"Person.name\":\"Rufino, José\",\"Person.identifier.orcid\":\"0000-0002-1344-8264\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Monteiro, Fernando C. Pinto, Cristina M. Rufino, José |
| datacite.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-01-14T09:40:14Z |
| datacite.date.embargoed.fl_str_mv | 2022-01-14T09:40:14Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| datacite.titles.title.fl_str_mv | Deep learning recognition of a large number of pollen grain types |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Monteiro, Fernando C. Pinto, Cristina M. Rufino, José |
| dc.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-01-14T09:40:14Z |
| dc.date.embargoed.fl_str_mv | 2022-01-14T09:40:14Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/24644 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Nature |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| dc.title.fl_str_mv | Deep learning recognition of a large number of pollen grain types |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Pollen in honey reflects its botanical origin and melissopalynology is used to identify origin, type and quantities of pollen grains of the botanical species visited by bees. Automatic pollen counting and classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. Despite the efforts made during the last decades, the manual classification process is still predominant. One of the reasons for that is the small number of types usually used in previous studies. In this paper, we present a large study to automatically identify pollen grains using nine state-of-the-art CNN techniques applied to the recently published POLEN73S image dataset. We observe that existing published approaches used original images without study the possible biased recognition due to pollen’s background colour or using preprocessing techniques. Our proposal manages to classify up to 97.4 % of the samples from the dataset with 73 different types of pollen. This result, which surpasses previous attempts in number and difficulty of pollen types under consideration, is an important step towards fully automatic pollen recognition, even with a large number of pollen grain types. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/4d22d92d-96bf-4626-9a7e-75d9ab775b57/download |
| id | ipb_9cd4252f2f547cab449a53d25593c8b2 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/24644 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/24644 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Monteiro, Fernando C. Monteiro, Fernando C. https://www.ciencia-id.pt/2019-BDBF-10E2 2019-BDBF-10E2 http://orcid.org/0000-0002-1421-8006 0000-0002-1421-8006 Pinto, Cristina M. Rufino, José Rufino, José https://www.ciencia-id.pt/C414-F47F-6323 C414-F47F-6323 http://orcid.org/0000-0002-1344-8264 0000-0002-1344-8264 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Springer Nature |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engSpringer Naturept_PTPollen in honey reflects its botanical origin and melissopalynology is used to identify origin, type and quantities of pollen grains of the botanical species visited by bees. Automatic pollen counting and classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. Despite the efforts made during the last decades, the manual classification process is still predominant. One of the reasons for that is the small number of types usually used in previous studies. In this paper, we present a large study to automatically identify pollen grains using nine state-of-the-art CNN techniques applied to the recently published POLEN73S image dataset. We observe that existing published approaches used original images without study the possible biased recognition due to pollen’s background colour or using preprocessing techniques. Our proposal manages to classify up to 97.4 % of the samples from the dataset with 73 different types of pollen. This result, which surpasses previous attempts in number and difficulty of pollen types under consideration, is an important step towards fully automatic pollen recognition, even with a large number of pollen grain types.application/pdfpt_PTDeep learning recognition of a large number of pollen grain typesPersonalMonteiro, Fernando C.DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78MonteiroFernando C.Ciência IDhttps://www.ciencia-id.pt2019-BDBF-10E2ORCIDhttp://orcid.org0000-0002-1421-8006Researcher IDhttps://www.researcherid.comH-9213-2016Scopus Author IDhttps://www.scopus.com8986162600Pinto, Cristina M.PersonalRufino, JoséDSpacehttp://dspace.org/items/1e24d2ce-a354-442a-bef8-eebadd94b385DSpacehttp://dspace.org/items/1e24d2ce-a354-442a-bef8-eebadd94b385RufinoJoséCiência IDhttps://www.ciencia-id.ptC414-F47F-6323ORCIDhttp://orcid.org0000-0002-1344-8264Scopus Author IDhttps://www.scopus.com55947199100Scopus Author IDhttps://www.scopus.com57188967176HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-303091884-2DOIIsPartOf10.1007/978-3-030-91885-9_282022-01-14T09:40:14Z20212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/24644http://purl.org/coar/access_right/c_abf2open accessConvolutional neural networkDeep learningImage segmentationPollen recognition3219604 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2021http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/4d22d92d-96bf-4626-9a7e-75d9ab775b57/download1st International Conference on Optimization, Learning Algorithms and Applications, OL2A 20211488381392Bragança |
| spellingShingle | Deep learning recognition of a large number of pollen grain types Monteiro, Fernando C. Convolutional neural network Deep learning Image segmentation Pollen recognition |
| status | SINGLETON |
| subject.fl_str_mv | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| title | Deep learning recognition of a large number of pollen grain types |
| title_full | Deep learning recognition of a large number of pollen grain types |
| title_fullStr | Deep learning recognition of a large number of pollen grain types |
| title_full_unstemmed | Deep learning recognition of a large number of pollen grain types |
| title_short | Deep learning recognition of a large number of pollen grain types |
| title_sort | Deep learning recognition of a large number of pollen grain types |
| topic | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| topic_facet | Convolutional neural network Deep learning Image segmentation Pollen recognition |
| url | http://hdl.handle.net/10198/24644 |
| visible | 1 |