|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agrossilvipastoril. |
|
Data corrente: |
26/02/2025 |
|
Data da última atualização: |
26/02/2025 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
DELLA-SILVA, J. L.; FALEIRO, V. de O.; PELISSARI, T. D.; FERREIRA, A.; DIAS, N. F.; SANTOS, D. H. dos; LOURENÇONI, T.; NAYARA, J.; MORINIGO, W. B.; TEODORO, L. P. R.; TEODORO, P. E.; SANTANA, D. C.; OLIVEIRA, I. C. de; SCHWINGEL, E. C.; SILVA, R. de A.; SILVA JUNIOR, C. A. da. |
|
Afiliação: |
JOÃO LUCAS DELLA-SILVA, UNIVERSIDADE DO ESTADO DE MATO GROSSO; VALERIA DE OLIVEIRA FALEIRO, CPAMT; TATIANE DEOTI PELISSARI, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; AMANDA FERREIRA, UNIVERSIDADE FEDERAL DO PIAUÍ; NEURIENNY FERREIRA DIAS, UNIVERSIDADE FEDERAL DE MATO GROSSO; DANIEL HENRIQUE DOS SANTOS, UNIVERSIDADE DO ESTADO DE MATO GROSSO; THAÍS LOURENÇONI, UNIVERSIDADE DO ESTADO DE MATO GROSSO; JOELMA NAYARA, UNIVERSIDADE DO ESTADO DE MATO GROSSO; WENDEL BUENO MORINIGO, UNIVERSIDADE DO ESTADO DE MATO GROSSO; LARISSA PEREIRA RIBEIRO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; PAULO EDUARDO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; DTHENIFER CORDEIRO SANTANA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; IZABELA CRISTINA DE OLIVEIRA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; ESTER CRISTINA SCHWINGEL, UNIVERSIDADE FEDERAL DE MATO GROSSO; RENAN DE ALMEIDA SILVA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; CARLOS ANTONIO DA SILVA JUNIOR, UNIVERSIDADE DO ESTADO DE MATO GROSSO. |
|
Título: |
Evaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques. |
|
Ano de publicação: |
2025 |
|
Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 37, Article number 101461, 2025. |
|
ISSN: |
2352-9385 |
|
DOI: |
https://doi.org/10.1016/j.rsase.2025.101461 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Abstract: Soybeans (Glycine max (L.) Merrill) are a major player in food security, and pest loss control is a major focus of research and technological development by the agricultural sector. Among these pests, Aphelenchoides besseyi contaminates the aerial part of the plant, which can be detected in the leaf's spectral response, based on in situ hyperspectral sensors with the adoption of remote sensing techniques, such as spectral models. Assessing such data using machine learning allows the identification of optimal computational conditions to evaluate different levels of infection by the green stem nematode in soybeans. Thus, this research aimed to (i) discriminate the spectral bands most sensitive to nematode infection, (ii) identify the spectral model with the greatest accuracy for distinguishing different levels of nematode infection according to reflectance, and (iii) verify the resilience to the impact of A. besseyi on soybeans. From this approach, the near and short-wave infrared spectral portions contributed most to discriminating different amounts of nematodes in the plant, in a scenario in which the logistic regression algorithm had greater performance. Finally, this evaluation suggests that the best discrimination conditions occur with data obtained in the final half of the soybean cultivation cycle. |
|
Thesagro: |
Aphelenchoides Besseyi; Espectrometria; Glycine Max; Nematóide; Sensoriamento Remoto; Soja. |
|
Thesaurus Nal: |
Hyperspectral imagery; Remote sensing; Soybeans; Spectroradiometers. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02679naa a2200445 a 4500
001 2173369
005 2025-02-26
008 2025 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2025.101461$2DOI
100 1 $aDELLA-SILVA, J. L.
245 $aEvaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques.$h[electronic resource]
260 $c2025
520 $aAbstract: Soybeans (Glycine max (L.) Merrill) are a major player in food security, and pest loss control is a major focus of research and technological development by the agricultural sector. Among these pests, Aphelenchoides besseyi contaminates the aerial part of the plant, which can be detected in the leaf's spectral response, based on in situ hyperspectral sensors with the adoption of remote sensing techniques, such as spectral models. Assessing such data using machine learning allows the identification of optimal computational conditions to evaluate different levels of infection by the green stem nematode in soybeans. Thus, this research aimed to (i) discriminate the spectral bands most sensitive to nematode infection, (ii) identify the spectral model with the greatest accuracy for distinguishing different levels of nematode infection according to reflectance, and (iii) verify the resilience to the impact of A. besseyi on soybeans. From this approach, the near and short-wave infrared spectral portions contributed most to discriminating different amounts of nematodes in the plant, in a scenario in which the logistic regression algorithm had greater performance. Finally, this evaluation suggests that the best discrimination conditions occur with data obtained in the final half of the soybean cultivation cycle.
650 $aHyperspectral imagery
650 $aRemote sensing
650 $aSoybeans
650 $aSpectroradiometers
650 $aAphelenchoides Besseyi
650 $aEspectrometria
650 $aGlycine Max
650 $aNematóide
650 $aSensoriamento Remoto
650 $aSoja
700 1 $aFALEIRO, V. de O.
700 1 $aPELISSARI, T. D.
700 1 $aFERREIRA, A.
700 1 $aDIAS, N. F.
700 1 $aSANTOS, D. H. dos
700 1 $aLOURENÇONI, T.
700 1 $aNAYARA, J.
700 1 $aMORINIGO, W. B.
700 1 $aTEODORO, L. P. R.
700 1 $aTEODORO, P. E.
700 1 $aSANTANA, D. C.
700 1 $aOLIVEIRA, I. C. de
700 1 $aSCHWINGEL, E. C.
700 1 $aSILVA, R. de A.
700 1 $aSILVA JUNIOR, C. A. da
773 $tRemote Sensing Applications: Society and Environment$gv. 37, Article number 101461, 2025.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agrossilvipastoril (CPAMT) |
|
