| Texto completo | |
| Autor(es): |
Medeiros, Andre Dantas de
[1]
;
Silva, Laercio Junio da
[1]
;
Ribeiro, Joao Paulo Oliveira
[1]
;
Ferreira, Kamylla Calzolari
[2]
;
Rosas, Jorge Tadeu Fim
[3]
;
Santos, Abraao Almeida
[4, 1]
;
Silva, Clissia Barboza da
[5]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] Univ Fed Vicosa, Agron Dept, BR-36570900 Vicosa, MG - Brazil
[2] Univ Fed Vicosa, Chem Dept, BR-36570900 Vicosa, MG - Brazil
[3] Univ Sao Paulo, Soil Sci Dept, BR-13418260 Piracicaba, SP - Brazil
[4] Univ Fed Vicosa, Dept Entomol, BR-36570900 Vicosa, MG - Brazil
[5] Univ Sao Paulo, Lab Radiobiol & Environm, Ctr Nucl Energy Agr, 303 Centenario Ave, BR-13416000 Piracicaba, SP - Brazil
Número total de Afiliações: 5
|
| Tipo de documento: | Carta |
| Fonte: | SENSORS; v. 20, n. 15 AUG 2020. |
| Citações Web of Science: | 3 |
| Resumo | |
Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizanthaseeds. (AU) | |
| Processo FAPESP: | 17/15220-7 - Métodos de análise de imagens não destrutivos para avaliação da qualidade de sementes |
| Beneficiário: | Clíssia Barboza Mastrangelo |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |