Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/11840 |
Resumo: | For higher productivity and economic yield in sugarcane field, several imaging techniques using sugarcane field images have been developed. However, the identification and measurement of gaps in sugarcane field crop rows are still commonly performed manually on site to decide to replant the gaps or the entire area. Manual measurement has a high cost of time and manpower. Based on these factors, this study aimed to create a new technique that automatically identifies and evaluates the gaps along the crop rows in aerial images of sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the remotely piloted aircraft were used to generate the orthomosaics of the crop field area and classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation of the planting rows in the image was found using the filter gradient Red Green Blue. Then, the crop rows were mapped using the curve adjustment method and overlap the classified image to detect and measure the gaps along the segment of the planting line. The technique developed obtained a maximum error of approximately 3% when compared to the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The proposed approach was able to properly identify the spatial position of automatically generated line segments over manually created line segments. The proposed method was also able to achieve statistically similar results when confronted with the technique performed manually in the image for the mapping of rows and identification of gaps for sugarcane fields with growth 40 and 80 days after planting. The automatic technique developed had a significant result in the evaluation of the gaps in the crop rows in the aerial images of sugarcane fields, thus, its use allows automated inspections with high accuracy measurements, and besides being able to assist producers in making decisions in the management of their sugarcane fields. |
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Soares, Fabrízzio Alphonsus Alves de Melo Nuneshttp://lattes.cnpq.br/7206645857721831Pedrini, Héliohttp://lattes.cnpq.br/9600140904712115 Nome completo do 2º coorientador(a): E-mail: Nomes completosSoares, Fabrízzio Alphonsus Alves de Melo NunesPedrini, HélioSalvini, Rogerio LopesCosta, Ronaldo Martins daCabacinha, Christian Diashttp://lattes.cnpq.br/7396087333582666Rocha, Bruno Moraes2022-01-12T10:26:27Z2022-01-12T10:26:27Z2021-12-09ROCHA, B. M. Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas. 2022. 121 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021.http://repositorio.bc.ufg.br/tede/handle/tede/11840ark:/38995/0013000000z91For higher productivity and economic yield in sugarcane field, several imaging techniques using sugarcane field images have been developed. However, the identification and measurement of gaps in sugarcane field crop rows are still commonly performed manually on site to decide to replant the gaps or the entire area. Manual measurement has a high cost of time and manpower. Based on these factors, this study aimed to create a new technique that automatically identifies and evaluates the gaps along the crop rows in aerial images of sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the remotely piloted aircraft were used to generate the orthomosaics of the crop field area and classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation of the planting rows in the image was found using the filter gradient Red Green Blue. Then, the crop rows were mapped using the curve adjustment method and overlap the classified image to detect and measure the gaps along the segment of the planting line. The technique developed obtained a maximum error of approximately 3% when compared to the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The proposed approach was able to properly identify the spatial position of automatically generated line segments over manually created line segments. The proposed method was also able to achieve statistically similar results when confronted with the technique performed manually in the image for the mapping of rows and identification of gaps for sugarcane fields with growth 40 and 80 days after planting. The automatic technique developed had a significant result in the evaluation of the gaps in the crop rows in the aerial images of sugarcane fields, thus, its use allows automated inspections with high accuracy measurements, and besides being able to assist producers in making decisions in the management of their sugarcane fields.Para obter maior produtividade e rendimento econômico no plantio de cana-de-açúcar, várias técnicas de processamento de imagens têm sido desenvolvidas. Entretanto, a identificação e a medição de falhas nas linhas de plantio de plantação de cana-de-açúcar ainda são comumente realizadas de forma manual no local (campo), para tomada de decisão de replantio apenas das falhas ou da área total. A medição manual tem um elevado custo de tempo e mão de obra. Com base nesses fatores, o objetivo deste trabalho foi propor uma abordagem que automaticamente identifica e avalia as falhas (gaps) ao longo das linhas de plantio em imagens aéreas de canaviais obtidas por uma pequena aeronave pilotada remotamente. As imagens capturadas com uso da aeronave pilotada remotamente foram utilizadas para gerar os ortomosaicos da área de plantio e classificadas com o algoritmo K - Vizinhos Mais Próximos para segmentar a linha de colheita. A orientação das linhas de plantio na imagem foi encontrada utilizando o filtro gradient Red Green Blue. Em seguida, as linhas de plantio foram mapeadas utilizando o método de ajuste de curvas e sobrepostas à imagem classificada para detectar e medir as falhas ao longo do segmento da linha de plantio. A técnica desenvolvida obteve um erro máximo de aproximadamente 3% quando comparada com o método manual para avaliar o comprimento linear das falhas nas linhas de plantio em um ortomosaico com uma área de 8,05 hectares por meio do método proposto por Stolf, adaptado para imagens digitais. A abordagem proposta conseguiu identificar apropriadamente a posição espacial dos segmentos de linhas gerados automaticamente sobre os segmentos de linha criados manualmente. O método proposto também foi capaz de alcançar resultados estatisticamente similares quando confrontados com a técnica realizada manualmente na imagem para o mapeamento das linhas e identificação das falhas, para as plantações de canade- açúcar com 40 e 80 dias após o plantio. A técnica desenvolvida teve resultado significativo na avaliação das falhas nas linhas de plantio nas imagens aéreas das plantações de cana-deaçúcar. Dessa forma, sua utilização permite inspeções automatizadas com medições de alta acurácia, auxiliando os produtores na tomada de decisão para o manejo de lavouras de canade- açúcar.Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2022-01-11T14:04:21Z No. of bitstreams: 2 Tese - Bruno Moraes Rocha - 2022.pdf: 32153393 bytes, checksum: 5e472f949ef0397b0c18d0eee59ef045 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2022-01-12T10:26:27Z (GMT) No. of bitstreams: 2 Tese - Bruno Moraes Rocha - 2022.pdf: 32153393 bytes, checksum: 5e472f949ef0397b0c18d0eee59ef045 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Made available in DSpace on 2022-01-12T10:26:27Z (GMT). No. of bitstreams: 2 Tese - Bruno Moraes Rocha - 2022.pdf: 32153393 bytes, checksum: 5e472f949ef0397b0c18d0eee59ef045 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Previous issue date: 2021-12-09porUniversidade Federal de GoiásPrograma de Pós-graduação em Ciência da Computação (INF)UFGBrasilInstituto de Informática - INF (RG)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessGradiente RGBPlantação de cana-de-açúcarAeronave pilotada remotamenteOrtomosaicoRGB gradientPlanting rowsRemotely piloted aircraftOrthomosaicCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAODetecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreasAutomatic detection and evaluation of sugarcane planting rowsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis2050050050026184reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/c18e3d70-7289-4bd4-a45f-9fc12a3dc27d/download8a4605be74aa9ea9d79846c1fba20a33MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.bc.ufg.br/tede/bitstreams/4c860a89-c524-4a95-a9a7-2fcbc1a7aa53/download4460e5956bc1d1639be9ae6146a50347MD52ORIGINALTese - Bruno Moraes Rocha - 2022.pdfTese - Bruno Moraes Rocha - 2022.pdfapplication/pdf32154944http://repositorio.bc.ufg.br/tede/bitstreams/aff3783b-f2f8-42da-add7-dc6513a0be30/download249bc83ce75b054f650aaabc8a8b20a7MD53tede/118402022-01-12 07:27:52.66http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accessoai:repositorio.bc.ufg.br:tede/11840http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2022-01-12T10:27:52Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.pt_BR.fl_str_mv |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
dc.title.alternative.eng.fl_str_mv |
Automatic detection and evaluation of sugarcane planting rows |
title |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
spellingShingle |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas Rocha, Bruno Moraes Gradiente RGB Plantação de cana-de-açúcar Aeronave pilotada remotamente Ortomosaico RGB gradient Planting rows Remotely piloted aircraft Orthomosaic CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
title_full |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
title_fullStr |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
title_full_unstemmed |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
title_sort |
Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas |
author |
Rocha, Bruno Moraes |
author_facet |
Rocha, Bruno Moraes |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Soares, Fabrízzio Alphonsus Alves de Melo Nunes |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7206645857721831 |
dc.contributor.advisor-co1.fl_str_mv |
Pedrini, Hélio |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/9600140904712115 Nome completo do 2º coorientador(a): E-mail: Nomes completos |
dc.contributor.referee1.fl_str_mv |
Soares, Fabrízzio Alphonsus Alves de Melo Nunes |
dc.contributor.referee2.fl_str_mv |
Pedrini, Hélio |
dc.contributor.referee3.fl_str_mv |
Salvini, Rogerio Lopes |
dc.contributor.referee4.fl_str_mv |
Costa, Ronaldo Martins da |
dc.contributor.referee5.fl_str_mv |
Cabacinha, Christian Dias |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7396087333582666 |
dc.contributor.author.fl_str_mv |
Rocha, Bruno Moraes |
contributor_str_mv |
Soares, Fabrízzio Alphonsus Alves de Melo Nunes Pedrini, Hélio Soares, Fabrízzio Alphonsus Alves de Melo Nunes Pedrini, Hélio Salvini, Rogerio Lopes Costa, Ronaldo Martins da Cabacinha, Christian Dias |
dc.subject.por.fl_str_mv |
Gradiente RGB Plantação de cana-de-açúcar Aeronave pilotada remotamente Ortomosaico |
topic |
Gradiente RGB Plantação de cana-de-açúcar Aeronave pilotada remotamente Ortomosaico RGB gradient Planting rows Remotely piloted aircraft Orthomosaic CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
RGB gradient Planting rows Remotely piloted aircraft Orthomosaic |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
For higher productivity and economic yield in sugarcane field, several imaging techniques using sugarcane field images have been developed. However, the identification and measurement of gaps in sugarcane field crop rows are still commonly performed manually on site to decide to replant the gaps or the entire area. Manual measurement has a high cost of time and manpower. Based on these factors, this study aimed to create a new technique that automatically identifies and evaluates the gaps along the crop rows in aerial images of sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the remotely piloted aircraft were used to generate the orthomosaics of the crop field area and classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation of the planting rows in the image was found using the filter gradient Red Green Blue. Then, the crop rows were mapped using the curve adjustment method and overlap the classified image to detect and measure the gaps along the segment of the planting line. The technique developed obtained a maximum error of approximately 3% when compared to the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The proposed approach was able to properly identify the spatial position of automatically generated line segments over manually created line segments. The proposed method was also able to achieve statistically similar results when confronted with the technique performed manually in the image for the mapping of rows and identification of gaps for sugarcane fields with growth 40 and 80 days after planting. The automatic technique developed had a significant result in the evaluation of the gaps in the crop rows in the aerial images of sugarcane fields, thus, its use allows automated inspections with high accuracy measurements, and besides being able to assist producers in making decisions in the management of their sugarcane fields. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-12-09 |
dc.date.accessioned.fl_str_mv |
2022-01-12T10:26:27Z |
dc.date.available.fl_str_mv |
2022-01-12T10:26:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
ROCHA, B. M. Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas. 2022. 121 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/11840 |
dc.identifier.dark.fl_str_mv |
ark:/38995/0013000000z91 |
identifier_str_mv |
ROCHA, B. M. Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas. 2022. 121 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021. ark:/38995/0013000000z91 |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/11840 |
dc.language.iso.fl_str_mv |
por |
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por |
dc.relation.program.fl_str_mv |
20 |
dc.relation.confidence.fl_str_mv |
500 500 500 |
dc.relation.department.fl_str_mv |
26 |
dc.relation.cnpq.fl_str_mv |
184 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Ciência da Computação (INF) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Informática - INF (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
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