{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T03:58:43Z","timestamp":1775534323661,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Key Research and Development Project","award":["20230201099GX"],"award-info":[{"award-number":["20230201099GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100\u20132498 nm. By utilizing seven preprocessing techniques, including moving average, S\u2013G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation.<\/jats:p>","DOI":"10.3390\/s24061855","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T05:43:08Z","timestamp":1710394988000},"page":"1855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"24","author":[{"given":"Hang","family":"Xue","sequence":"first","affiliation":[{"name":"College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"College of Electronic and Information Engineering, Beihua University, Jilin 132021, China"}]},{"given":"Xiping","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}]},{"given":"Dongmei","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Beihua University, Jilin 132021, China"}]},{"given":"Guocheng","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Beihua University, Jilin 132021, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"key":"ref_1","first-page":"126","article-title":"Storage techniques and selection methods for maize seeds","volume":"10","author":"Zhou","year":"2020","journal-title":"Sci. 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