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Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40\u00b0C for 36\u00a0h, whereas the other batch was stored under favorable conditions. Watermelon seeds\u2019 spectral images were captured using an HSI with a charge\u2010coupled device camera ranging from 400 to 1000\u00a0nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid\u2010SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (\n                      <jats:italic>c<\/jats:italic>\n                      ,\n                      <jats:italic>g<\/jats:italic>\n                      ) set at 13.17 and 0.01, respectively, and a runtime of 4.19328\u00a0s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA\u2010ABC\u2010SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical Application<\/jats:title>\n                    <jats:p>The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time\u2010consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.17151","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T23:49:36Z","timestamp":1719013776000},"page":"4403-4418","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Rapid and nondestructive watermelon (\n                    <i>Citrullus lanatus<\/i>\n                    ) seed viability detection based on visible near\u2010infrared hyperspectral imaging technology and machine learning algorithms"],"prefix":"10.1111","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0331-5468","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering Jiangsu University  Zhenjiang Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6335-7160","authenticated-orcid":false,"given":"Adria","family":"Nirere","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering Jiangsu University  Zhenjiang Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keza Dominique","family":"Dusabe","sequence":"additional","affiliation":[{"name":"School of Food Science and Biological Engineering Jiangsu University  Zhenjiang Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Yuhao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering Jiangsu University  Zhenjiang Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guverinoma","family":"Adrien","sequence":"additional","affiliation":[{"name":"School of Food Science and Engineering Jiangsu University of Science and Technology  Zhenjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.4103\/0973\u20101296.108142"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9194119"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2016.01.010"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104650"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.108247"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods11131936"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods12152957"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1039\/C4AN00730A"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/rec.13174"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-5571-3"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/jfbc.13763"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1021\/jf001117"},{"key":"e_1_2_10_15_1","unstructured":"Flinn. 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