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A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.
Batista, Thiago Barbosa; Mastrangelo, Clíssia Barboza; de Medeiros, André Dantas; Petronilio, Ana Carolina Picinini; Fonseca de Oliveira, Gustavo Roberto; Dos Santos, Isabela Lopes; Crusciol, Carlos Alexandre Costa; Amaral da Silva, Edvaldo Aparecido.
Affiliation
  • Batista TB; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
  • Mastrangelo CB; Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil.
  • de Medeiros AD; Department of Agronomy, Federal University of Viçosa, Viçosa, Brazil.
  • Petronilio ACP; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
  • Fonseca de Oliveira GR; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
  • Dos Santos IL; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
  • Crusciol CAC; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
  • Amaral da Silva EA; Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil.
Front Plant Sci ; 13: 914287, 2022.
Article in En | MEDLINE | ID: mdl-35774807
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Front Plant Sci Year: 2022 Document type: Article Affiliation country: Brazil Country of publication: Switzerland
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Front Plant Sci Year: 2022 Document type: Article Affiliation country: Brazil Country of publication: Switzerland