UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/242674 |
Resumo: | Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products. |
id |
UNSP_46807126885889ca5e6e6c67aa13d0c5 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/242674 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcaneRemote sensingSugarcaneRipeningPredicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Versão final do editorUniversidade Estadual Paulista (Unesp)CAPES: 001Frontiers MediaUniversidade Estadual Paulista (Unesp)Barbosa Júnior, Marcelo Rodrigues [UNESP]Moreira, Bruno Rafael de Almeida [UNESP]Oliveira, Romário Porto de [UNESP]Shiratsuchi, Luciano ShozoSilva, Rouverson Pereira [UNESP]2023-03-27T17:02:04Z2023-03-27T17:02:04Z2023-01-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfFrontiers in Plant Science, v. 14, 2023.1664-462Xhttp://hdl.handle.net/11449/24267410.3389/fpls.2023.1114852794975792096423105619499946859154846711655204294919195847468119281833574819290770000-0002-7207-21560000-0002-8686-40820000-0001-5458-90820000-0002-1986-64320000-0001-8852-2548engFrontiers in Plant Scienceinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESP2024-06-06T15:18:03Zoai:repositorio.unesp.br:11449/242674Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:44:45.834346Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
title |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
spellingShingle |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane Barbosa Júnior, Marcelo Rodrigues [UNESP] Remote sensing Sugarcane Ripening |
title_short |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
title_full |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
title_fullStr |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
title_full_unstemmed |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
title_sort |
UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane |
author |
Barbosa Júnior, Marcelo Rodrigues [UNESP] |
author_facet |
Barbosa Júnior, Marcelo Rodrigues [UNESP] Moreira, Bruno Rafael de Almeida [UNESP] Oliveira, Romário Porto de [UNESP] Shiratsuchi, Luciano Shozo Silva, Rouverson Pereira [UNESP] |
author_role |
author |
author2 |
Moreira, Bruno Rafael de Almeida [UNESP] Oliveira, Romário Porto de [UNESP] Shiratsuchi, Luciano Shozo Silva, Rouverson Pereira [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Barbosa Júnior, Marcelo Rodrigues [UNESP] Moreira, Bruno Rafael de Almeida [UNESP] Oliveira, Romário Porto de [UNESP] Shiratsuchi, Luciano Shozo Silva, Rouverson Pereira [UNESP] |
dc.subject.por.fl_str_mv |
Remote sensing Sugarcane Ripening |
topic |
Remote sensing Sugarcane Ripening |
description |
Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-27T17:02:04Z 2023-03-27T17:02:04Z 2023-01-26 |
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 |
Frontiers in Plant Science, v. 14, 2023. 1664-462X http://hdl.handle.net/11449/242674 10.3389/fpls.2023.1114852 7949757920964231 0561949994685915 4846711655204294 9191958474681192 8183357481929077 0000-0002-7207-2156 0000-0002-8686-4082 0000-0001-5458-9082 0000-0002-1986-6432 0000-0001-8852-2548 |
identifier_str_mv |
Frontiers in Plant Science, v. 14, 2023. 1664-462X 10.3389/fpls.2023.1114852 7949757920964231 0561949994685915 4846711655204294 9191958474681192 8183357481929077 0000-0002-7207-2156 0000-0002-8686-4082 0000-0001-5458-9082 0000-0002-1986-6432 0000-0001-8852-2548 |
url |
http://hdl.handle.net/11449/242674 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Frontiers in Plant Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Media |
publisher.none.fl_str_mv |
Frontiers Media |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128556178341888 |