{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:46:58Z","timestamp":1775886418169,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T00:00:00Z","timestamp":1596412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S\u00e3o Paulo Research Foundation -FAPESP","award":["Grant#2017\/15220-7"],"award-info":[{"award-number":["Grant#2017\/15220-7"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior \u2013 CAPES","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s20154319","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T06:16:47Z","timestamp":1596435407000},"page":"4319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-0292","authenticated-orcid":false,"given":"Andr\u00e9 Dantas de","family":"Medeiros","sequence":"first","affiliation":[{"name":"Agronomy Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7202-0420","authenticated-orcid":false,"given":"La\u00e9rcio Junio da","family":"Silva","sequence":"additional","affiliation":[{"name":"Agronomy Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1163-3546","authenticated-orcid":false,"given":"Jo\u00e3o Paulo Oliveira","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Agronomy Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"}]},{"given":"Kamylla Calzolari","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Chemistry Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3244-4816","authenticated-orcid":false,"given":"Jorge Tadeu Fim","family":"Rosas","sequence":"additional","affiliation":[{"name":"Soil Science Department, University of S\u00e3o Paulo, Piracicaba SP 13418-260, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-3294","authenticated-orcid":false,"given":"Abra\u00e3o Almeida","family":"Santos","sequence":"additional","affiliation":[{"name":"Agronomy Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"},{"name":"Entomology Department, Federal University of Vi\u00e7osa, Vi\u00e7osa MG 36570-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7279-3260","authenticated-orcid":false,"given":"Cl\u00edssia Barboza da","family":"Silva","sequence":"additional","affiliation":[{"name":"Laboratory of Radiobiology and Environment, University of S\u00e3o Paulo-Center for Nuclear Energy in Agriculture, 303 Centen\u00e1rio Avenue, Piracicaba SP 13416-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1093\/jxb\/erv490","article-title":"Seed vigour and crop establishment: Extending performance beyond adaptation","volume":"67","author":"Bassel","year":"2016","journal-title":"J. Exp. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"ElMasry, G., Mandour, N., Al-Rejaie, S., Belin, E., and Rousseau, D. (2019). Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring\u2014An Overview. Sensors, 19.","DOI":"10.3390\/s19051090"},{"key":"ref_3","first-page":"35","article-title":"Recent advances in emerging techniques for non-destructive detection of seed viability: A review","volume":"1","author":"Xia","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.snb.2017.08.036","article-title":"Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics","volume":"255","author":"Wakholi","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.biosystemseng.2018.09.015","article-title":"X-ray CT image analysis for morphology of muskmelon seed in relation to germination","volume":"175","author":"Ahmed","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112162","DOI":"10.1016\/j.indcrop.2020.112162","article-title":"Quality classification of Jatropha curcas seeds using radiographic images and machine learning","volume":"146","author":"Pinheiro","year":"2020","journal-title":"Ind. Crops Prod."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"De Medeiros, A.D., Zavala-Le\u00f3n, M.J., da Silva, L.J., Oliveira, A.M.S., and dos Dias, D.C.F. (2020). Relationship between internal morphology and physiological quality of pepper seeds during fruit maturation and storage. Agron. J.","DOI":"10.1002\/agj2.20071"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"592","DOI":"10.2134\/agronj2018.05.0302","article-title":"Evaluation of the desiccation of campomanesia adamantium seed using radiographic analysis and the relation with physiological potential","volume":"111","author":"Peixoto","year":"2019","journal-title":"Agron. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1002\/jsfa.8646","article-title":"Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy","volume":"98","author":"Kusumaningrum","year":"2018","journal-title":"J. Sci. Food Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.5307\/JBE.2016.41.1.051","article-title":"Non-Destructive Sorting Techniques for Viable Pepper (Capsicum annuum L.) Seeds Using Fourier Transform Near-Infrared and Raman Spectroscopy","volume":"41","author":"Seo","year":"2016","journal-title":"J. Biosyst. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e03477","DOI":"10.1016\/j.heliyon.2020.e03477","article-title":"Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)","volume":"6","author":"Andrade","year":"2020","journal-title":"Heliyon"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.snb.2015.10.082","article-title":"Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy","volume":"224","author":"Ambrose","year":"2016","journal-title":"Sens. Actuators B Chem."},{"key":"ref_13","unstructured":"Silverstein, R.M., Webster, F.X., and Kiemle, D. (2005). Spectrometric Identification of Organic Compounds, John Wiley & Sons, Inc.. [7th ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.foodchem.2016.11.064","article-title":"Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods","volume":"221","author":"Li","year":"2017","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.infrared.2019.02.008","article-title":"Determination of viability of Retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy","volume":"98","author":"Mukasa","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, G.L. (2020). Comparison and Application of Non-Destructive NIR Evaluations of Seed Protein and Oil Content in Soybean Breeding. Agronomy, 10.","DOI":"10.3390\/agronomy10010077"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.aca.2018.04.004","article-title":"Near infrared spectroscopy: A mature analytical technique with new perspectives\u2014A review","volume":"1026","author":"Pasquini","year":"2018","journal-title":"Anal. Chim. Acta"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.postharvbio.2018.12.016","article-title":"Non-destructive porosity mapping of fruit and vegetables using X-ray CT","volume":"150","author":"Nugraha","year":"2019","journal-title":"Postharvest Biol. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aca.2015.04.042","article-title":"Data fusion methodologies for food and beverage authentication and quality assessment\u2014A review","volume":"891","author":"Mestres","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114212","DOI":"10.1016\/j.geoderma.2020.114212","article-title":"Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy","volume":"365","author":"Benedet","year":"2020","journal-title":"Geoderma"},{"key":"ref_21","unstructured":"Stevens, A., and Ramirez\u2013Lopez, L. (2020, February 02). An Introduction to the Prospectr Package. Available online: https:\/\/cran.r-project.org\/web\/packages\/prospectr\/vignettes\/prospectr-intro.pdf."},{"key":"ref_22","unstructured":"R Core Team (2019). R Development Core Team. R Lang. Environ. Stat. Comput., 55, 275\u2013286."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105555","DOI":"10.1016\/j.compag.2020.105555","article-title":"IJCropSeed: An open-access tool for high-throughput analysis of crop seed radiographs","volume":"175","author":"Pereira","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","unstructured":"Mapa, M. (2009). Rules for Seed Analysis, Secretaria de Defesa Agropecu\u00e1ria."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. J. Stat. Softw., 28.","DOI":"10.18637\/jss.v028.i05"},{"key":"ref_26","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s10535-007-0159-9","article-title":"Pepper seed germination assessed by combined X-radiography and computer-aided imaging analysis","volume":"51","year":"2007","journal-title":"Biol. Plant."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1590\/S0103-90162011000400004","article-title":"Relationship between germination and bell pepper seed structure assessed by the X-ray test","volume":"68","author":"Gagliardi","year":"2011","journal-title":"Sci. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/05704928.2016.1157808","article-title":"Infrared spectroscopy combined with imaging: A new developing analytical tool in health and plant science","volume":"51","author":"Kumar","year":"2016","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.vibspec.2006.06.001","article-title":"Identification and quantification of valuable plant substances by IR and Raman spectroscopy","volume":"43","author":"Schulz","year":"2007","journal-title":"Vib. Spectrosc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compag.2015.06.010","article-title":"Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening","volume":"116","author":"Dumont","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103213","DOI":"10.1016\/j.infrared.2020.103213","article-title":"Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies","volume":"105","author":"Fan","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, X., Feng, X., Sun, D., Liu, F., Bao, Y., and He, Y. (2019). Rapid and nondestructive measurement of rice seed vitality of different years using near-infrared hyperspectral imaging. Molecules, 24.","DOI":"10.3390\/molecules24122227"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bao, Y., Mi, C., Wu, N., Liu, F., and He, Y. (2019). Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics. Appl. Sci., 9.","DOI":"10.3390\/app9194119"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Baek, I., Kusumaningrum, D., Kandpal, L.M., Lohumi, S., Mo, C., Kim, M.S., and Cho, B.K. (2019). Rapid measurement of soybean seed viability using Kernel-based multispectral image analysis. Sensors, 19.","DOI":"10.3390\/s19020271"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:52Z","timestamp":1760176432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,3]]},"references-count":35,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20154319"],"URL":"https:\/\/doi.org\/10.3390\/s20154319","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,3]]}}}