A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de Viçosa (UFV)-
Autor(es): dc.creatorBatista, Thiago Barbosa-
Autor(es): dc.creatorMastrangelo, Clissia Barboza-
Autor(es): dc.creatorMedeiros, Andre Dantas de-
Autor(es): dc.creatorPetronilio, Ana Carolina Picinini-
Autor(es): dc.creatorOliveira, Gustavo Roberto Fonseca de-
Autor(es): dc.creatorSantos, Isabela Lopes dos-
Autor(es): dc.creatorCrusciol, Carlos Alexandre Costa-
Autor(es): dc.creatorSilva, Edvaldo Aparecido Amaral da-
Data de aceite: dc.date.accessioned2025-08-21T18:21:18Z-
Data de disponibilização: dc.date.available2025-08-21T18:21:18Z-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-06-14-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fpls.2022.914287-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/237767-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/237767-
Descrição: dc.descriptionIn recent years, technological innovations have allowed significant advances in the diagnosis of seed quality. Seeds with superior physiological quality are those with the highest level of physiological maturity and the integration of rapid and precise methods to separate them contributes to better performance in the field. Autofluorescence-spectral imaging is an innovative technique based on fluorescence signals from fluorophores present in seed tissues, which have biological implications for seed quality. Thus, through this technique, it would be possible to classify seeds in different maturation stages. To test this, we produced plants of a commercial cultivar (MG/BR 46 Conquista) and collected the seeds at five reproductive (R) stages: R7.1 (beginning of maturity), R7.2 (mass maturity), R7.3 (seed disconnected from the mother plant), R8 (harvest point), and R9 (final maturity). Autofluorescence signals were extracted from images captured at different excitation/emission combinations. In parallel, we investigated physical parameters, germination, vigor and the dynamics of pigments in seeds from different maturation stages. To verify the accuracy in predicting the seed maturation stages based on autofluorescence-spectral imaging, we created machine learning models based on three algorithms: (i) random forest, (ii) neural network, and (iii) support vector machine. Here, we reported the unprecedented use of the autofluorescence-spectral technique to classify the maturation stages of soybean seeds, especially using the excitation/emission combination of chlorophyll a (660/700 nm) and b (405/600 nm). Taken together, the machine learning algorithms showed high performance segmenting the different stages of seed maturation. In summary, our results demonstrated that the maturation stages of soybean seeds have their autofluorescence-spectral identity in the wavelengths of chlorophylls, which allows the use of this technique as a marker of seed maturity and superior physiological quality.-
Descrição: dc.descriptionSao Paulo State Univ, Coll Agr Sci, Dept Crop Sci, Botucatu, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Ctr Nucl Energy Agr, Lab Radiobiol & Environm, Piracicaba, Brazil-
Descrição: dc.descriptionUniv Fed Vicosa, Dept Agron, Vicosa, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Coll Agr Sci, Dept Crop Sci, Botucatu, Brazil-
Formato: dc.format14-
Idioma: dc.languageen-
Publicador: dc.publisherFrontiers Media Sa-
Relação: dc.relationFrontiers In Plant Science-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectSeed maturity-
Palavras-chave: dc.subjectSeed quality-
Palavras-chave: dc.subjectSupport vector machine-
Palavras-chave: dc.subjectChlorophyll fluorescence-
Palavras-chave: dc.subjectGlycine max-
Título: dc.titleA Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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