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A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.
Front Plant Sci. 2022 Jun 14;13:914287. doi: 10.3389/fpls.2022.914287. eCollection 2022.
Front Plant Sci. 2022.
PMID: 35774807
Free PMC article.
An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.
Fonseca de Oliveira GR, Mastrangelo CB, Hirai WY, Batista TB, Sudki JM, Petronilio ACP, Crusciol CAC, Amaral da Silva EA.
Fonseca de Oliveira GR, et al.
Front Plant Sci. 2022 Apr 14;13:849986. doi: 10.3389/fpls.2022.849986. eCollection 2022.
Front Plant Sci. 2022.
PMID: 35498679
Free PMC article.
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Quality of cowpea seeds: A food security strategy in the tropical environment.
Moreno LA, Fonseca de Oliveira GR, Batista TB, Bossolani JW, Ducatti KR, Guimarães CC, Amaral da Silva EA.
Moreno LA, et al. Among authors: fonseca de oliveira gr.
PLoS One. 2022 Oct 14;17(10):e0276136. doi: 10.1371/journal.pone.0276136. eCollection 2022.
PLoS One. 2022.
PMID: 36240183
Free PMC article.
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Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.
Sudki JM, Fonseca de Oliveira GR, de Medeiros AD, Mastrangelo T, Arthur V, Amaral da Silva EA, Mastrangelo CB.
Sudki JM, et al. Among authors: fonseca de oliveira gr.
Front Plant Sci. 2023 Feb 22;14:1112916. doi: 10.3389/fpls.2023.1112916. eCollection 2023.
Front Plant Sci. 2023.
PMID: 36909395
Free PMC article.
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