Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Sensors

LaércioJunio da Silva¹  AbraãoAlmeida Santos¹  AndréDantas de Medeiros¹  JoãoPaulo Oliveira Ribeiro¹  KamyllaCalzolari Ferreira²  ClíssiaBarboza da Silva³  JorgeTadeu Fim Rosas⁴ 

[1] Agronomy Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil;Chemistry Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil;Laboratory of Radiobiology and Environment, University of São Paulo-Center for Nuclear Energy in Agriculture, 303 Centenário Avenue, Piracicaba SP 13416-000, Brazil;Soil Science Department, University of São Paulo, Piracicaba SP 13418-260, Brazil;

关键词: germination prediction;    linear discriminant analysis;    Fourier transform near-infrared spectroscopy;    radiographic images;    Urochloa brizantha;   

DOI  :  10.3390/s20154319

来源: DOAJ

【 摘 要 】

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. brizantha seeds.

【 授权许可】

Unknown