Abstract
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.
Highlights
Seed quality is an important factor in agricultural production, with a direct impact on yield [1].In plant breeding, the use of high-quality seeds reduces costs of field experiments and increases the probability to identify a better crop variety
We presented a new methodology based on merged data to predict germination capacity and seed vigor using Fourier transform near-infrared (FT-NIR) and X-ray images, which was validated using seeds of U. brizantha
Spectroscopy and X-ray imaging predict based on grayscale simplify the the methods tested in this paper. toWe observed seed quality traits
Summary
Seed quality is an important factor in agricultural production, with a direct impact on yield [1].In plant breeding, the use of high-quality seeds reduces costs of field experiments and increases the probability to identify a better crop variety. Quality assurance programs rely on numerous methods to certify seed quality attributes, such as germination and vigor tests [2]. These procedures have limitations related to time consumption, subjectivity, and the destructive nature. FT-NIR spectroscopy is based on the absorption of electromagnetic radiation at wavelengths ranging from 780 to 2500 nm [13]. It offers versatility for direct and simultaneous measurements of several constituents in seed samples [10,14,15,16,17]
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