{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T06:27:28Z","timestamp":1787120848817,"version":"build-2736575974"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Watermelon seed purity is a critical determinant of varietal authenticity and production quality. In hybrid seed production, however, distinguishing between maternal seeds and hybrid seeds at the single-seed level remains challenging because their phenotypic differences are subtle. Conventional purity identification methods are often destructive, labor-intensive, and time-consuming and are poorly suited to the practical demand for rapid, continuous, single-seed online sorting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, this study developed the Watermelon Seed Purity Sorter (WSPSorter), an integrated real-time sorting system based on rotary singulation, near-infrared (NIR) spectroscopy, and pneumatic actuation. The system achieves stable single-seed delivery through a rotary seed-metering mechanism, acquires single-seed spectral information in real time, and combines NIR spectroscopy with an Extreme Gradient Boosting (XGBoost) classification model to discriminate maternal seeds from hybrid seeds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The optimal model, combining wavelet denoising, the successive projections algorithm (SPA), and XGBoost, achieved accuracies of 98.89% on the validation set and 97.78% on the independent test set. WSPSorter achieved an average inter-sample interval of 254.88 ms, corresponding to a practical throughput of approximately 4.0 seeds per second. Sorting performance, evaluated against PCR-derived reference labels, reached 94.04% for maternal seeds and 90.05% for hybrid seeds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results indicate that WSPSorter effectively connects single-seed spectral sensing with real-time physical sorting, providing a feasible engineering solution for rapid, non-destructive, and automated purity sorting of watermelon seeds.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1909902","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:45:36Z","timestamp":1787118336000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["WSPSorter: a real-time integrated system for non-destructive purity sorting of watermelon seeds based on rotary singulation and near infrared spectroscopy"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhichao","family":"Ni","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiakai","family":"Wan","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Watermelon and Cabbage Digital Seed Industry, Ministry of Agriculture and Rural Affairs, P.R. China, Ningbo Weimeng Seed Industry Co., Ltd.","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zebing","family":"Song","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangxu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Bai","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Watermelon and Cabbage Digital Seed Industry, Ministry of Agriculture and Rural Affairs, P.R. China, Ningbo Weimeng Seed Industry Co., Ltd.","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Ara\u00fajo","year":"2001","journal-title":"Chemometrics Intelligent Lab. Syst."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.914287","article-title":"A reliable method to recognize soybean seed maturation stages based on autofluorescence-spectral imaging combined with machine learning algorithms","volume":"13","author":"Batista","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B3","first-page":"53","article-title":"A novel seed sorting device utilizing vertical centrifugation and spectroscopy techniques","author":"Chen","year":"2024"},{"key":"B4","first-page":"785","article-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"B5","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thresholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inf. Theory"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3390\/app10186569","article-title":"Online application of a hyperspectral imaging system for the sorting of adulterated almonds","volume":"10","author":"Faqeerzada","year":"2020","journal-title":"Appl. Sci."},{"key":"B7","article-title":"Faostat database: Production of watermelons. Crops and livestock products domain, Item Code 01221, Element Code 5510","year":"2024"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3390\/plants12051177","article-title":"Crop seed phenomics: focus on non-destructive functional trait phenotyping methods and applications","volume":"12","author":"Hacisalihoglu","year":"2023","journal-title":"Plants"},{"key":"B9","doi-asserted-by":"publisher","first-page":"58","DOI":"10.31015\/jaefs.2022.1.9","article-title":"Microsatellite analysis in some watermelon (citrullus lanatus) genotypes","volume":"6","author":"Kama\u015fak","year":"2022","journal-title":"Int. J. Agric. Environ. Food. Sci."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12071600","article-title":"Estimation of optimum vacuum pressure of air-suction seed-metering device of precision seeders using artificial neural network models","volume":"12","author":"Karayel","year":"2022","journal-title":"Agronomy"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1002\/jsfa.7832","article-title":"Non-destructive evaluation of bacteria-infected watermelon seeds using visible\/near-infrared hyperspectral imaging","volume":"97","author":"Lee","year":"2017","journal-title":"J. Sci. Food Agric."},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1726266","article-title":"Rapid detection method of soybean seed germination potential based on the plsr-mlp fusion model","volume":"16","author":"Liu","year":"2026","journal-title":"Front. Plant Sci."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1007\/s10812-019-00757-w","article-title":"Rapid discrimination of high-quality watermelon seeds by multispectral imaging combined with chemometric methods","volume":"85","author":"Liu","year":"2019","journal-title":"J. Appl. Spectrosc."},{"key":"B14","doi-asserted-by":"crossref","DOI":"10.1088\/1742-6596\/1769\/1\/012013","article-title":"A review of artificial intelligence methods for seed quality inspection based on spectral imaging and analysis","author":"Long","year":"2021"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1007\/s12041-018-1027-4","article-title":"Identification of high-efficiency ssr markers for assessing watermelon genetic purity","volume":"97","author":"Lu","year":"2018","journal-title":"J. Genet."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2023.105060","article-title":"Discrimination of thai melon seeds using near-infrared spectroscopy and adaptive self-organizing maps","volume":"245","author":"Makmuang","year":"2024","journal-title":"Chemometrics Intelligent Lab. Syst."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0291105","article-title":"Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry","volume":"18","author":"Matsuda","year":"2023","journal-title":"PloS One"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.108974","article-title":"Deep learning application for real-time gravity-assisted seed conveying system for watermelon seeds purity sorting","volume":"221","author":"Mukasa","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"B19","volume-title":"Practical NIR Spectroscopy With Applications in Food and Beverage Analysis","author":"Osborne","year":"1993"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1283921","article-title":"Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning","volume":"14","author":"Qi","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3390\/agriengineering7090309","article-title":"A comprehensive systematic review of precision planting mechanisation for sesame: Agronomic challenges, technological advances, and integration of simulation-based optimisation","volume":"7","author":"Raveendran","year":"2025","journal-title":"AgriEngineering"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3390\/s23041820","article-title":"Single seed near-infrared hyperspectral imaging for classification of perennial ryegrass seed","volume":"23","author":"Reddy","year":"2023","journal-title":"Sensors"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.5935\/1806-6690.20240005","article-title":"Near infrared spectroscopy for the classification of vigor level of soybean seed","volume":"55","author":"Silva","year":"2023","journal-title":"Rev. Ciencia Agronomica"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1089759","article-title":"Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis","volume":"14","author":"Sousa","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B25","article-title":"Seeds of gourd and vegetable crops - part 1: Melons. GB 16715.1-2010. In Chinese","year":"2010"},{"key":"B26","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s13007-022-00918-7","article-title":"A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning","volume":"18","author":"Tu","year":"2022","journal-title":"Plant Methods"},{"key":"B27","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3964\/j.issn.1000-0593(2021)01-0052-08","article-title":"Research progress on non-destructive and rapid detection of seed quality based on spectroscopy and imaging technologies","volume":"41","author":"Wang","year":"2021","journal-title":"Spectrosc. Spectral Anal."},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2023.104611","article-title":"Variety identification of sweet maize seeds based on hyperspectral imaging combined with deep learning","volume":"130","author":"Wang","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108378","article-title":"Seedscreener: A novel integrated wheat germplasm phenotyping platform based on nir-feature detection and 3d-reconstruction","volume":"215","author":"Wu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1047479","article-title":"Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models","volume":"13","author":"Wu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2026.1746869","article-title":"Near-infrared prediction of tannin content in walnut kernels using wavelet transform combined with interpretable machine learning models","volume":"17","author":"Xia","year":"2026","journal-title":"Front. Plant Sci."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2025.112759","article-title":"A review on spectral data preprocessing techniques for machine learning and quantitative analysis","volume":"28","author":"Yan","year":"2025","journal-title":"IScience"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1361328","article-title":"Effect of genetic distances of different genotypes of maize on the authenticity of single seeds detected by nir spectroscopy","volume":"15","author":"Yang","year":"2024","journal-title":"Front. Plant Sci."},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1398762","article-title":"Prediction of protein content in paddy rice (oryza sativa l.) combining near-infrared spectroscopy and deep-learning algorithm","volume":"15","author":"Yang","year":"2024","journal-title":"Front. Plant Sci."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.986754","article-title":"Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds","volume":"13","author":"Yasmin","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3390\/s19051190","article-title":"Classification method for viability screening of naturally aged watermelon seeds using ft-nir spectroscopy","volume":"19","author":"Yasmin","year":"2019","journal-title":"Sensors"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1015891","article-title":"Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology","volume":"13","author":"Zhang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1717851","article-title":"Geographic authentication of amomum tsaoko seeds using fourier transform-near infrared spectroscopy combined with machine learning techniques and feature reduction analysis","volume":"16","author":"Zheng","year":"2026","journal-title":"Front. Plant Sci."},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.3390\/foods13101570","article-title":"Discrimination of new and aged seeds based on on-line near-infrared spectroscopy technology combined with machine learning","volume":"13","author":"Zhu","year":"2024","journal-title":"Foods"}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1909902\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:45:38Z","timestamp":1787118338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1909902\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,19]]},"references-count":39,"alternative-id":["10.3389\/fpls.2026.1909902"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1909902","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,19]]},"article-number":"1909902"}}