Identifying Nematode Damage on Soybean through Remote Sensing and Machine Learning Techniques

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorKansas State University-
Autor(es): dc.contributorMiller Plant Sciences-
Autor(es): dc.contributorAuburn University-
Autor(es): dc.creatorSantos, Letícia Bernabé-
Autor(es): dc.creatorBastos, Leonardo Mendes-
Autor(es): dc.creatorde Oliveira, Mailson Freire-
Autor(es): dc.creatorSoares, Pedro Luiz Martins-
Autor(es): dc.creatorCiampitti, Ignacio Antonio-
Autor(es): dc.creatorda Silva, Rouverson Pereira-
Data de aceite: dc.date.accessioned2025-08-21T16:41:46Z-
Data de disponibilização: dc.date.available2025-08-21T16:41:46Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12102404-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249296-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249296-
Descrição: dc.descriptionIdentifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants.-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences São Paulo State University ‘Júlio de Mesquita Filho’ (UNESP) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane, SP-
Descrição: dc.descriptionDepartment of Agronomy Kansas State University, 1712 Claflin Road-
Descrição: dc.descriptionDepartment of Crop and Soil Sciences University of Georgia Miller Plant Sciences-
Descrição: dc.descriptionDepartment of Crop Soil and Environmental Sciences Auburn University, 350 S College St-
Descrição: dc.descriptionDepartment of Agricultural Production Sciences São Paulo State University ‘Júlio de Mesquita Filho’ (UNESP) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane, SP-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences São Paulo State University ‘Júlio de Mesquita Filho’ (UNESP) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane, SP-
Descrição: dc.descriptionDepartment of Agricultural Production Sciences São Paulo State University ‘Júlio de Mesquita Filho’ (UNESP) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane, SP-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdigital agriculture-
Palavras-chave: dc.subjectdisease detection-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmultispectral mapping-
Palavras-chave: dc.subjectnematodes-
Palavras-chave: dc.subjectremote sensing-
Título: dc.titleIdentifying Nematode Damage on Soybean through Remote Sensing and Machine Learning Techniques-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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