{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:03:00Z","timestamp":1789970580954,"version":"4.0.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"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:p>The growth year of cultivated ginseng is an important factor influencing its quality and price. This study established a dataset comprising leaves from 1- to 5-year-old cultivated ginseng plants. With the aim of distinguishing the growth years of cultivated ginseng plants, a machine-learning and deep-learning dual-branch fusion non-destructive identification model was developed. In the machine-learning branch, 19 spectral features were derived using the leaf hyperspectral data and an SVM identification model was built. The accuracy of this model was 90.89%. The accuracy of the SVM model was improved to 94.58% through the extraction of six RGB image-derived phenotypic features and the use of feature fusion with spectral data. In order to learn deeper features in the images, the ResNet18 model was created in the deep-learning branch based on RGB images. Finally, the predictions of the feature-fusion-based SVM model and the ResNet18 model were combined with the help of decision weighted voting fusion, which gave the overall accuracy of 96.80%. It is an increase of 5.91 percentage points when compared to the spectral-feature-based SVM model and an increase of 2.22 percentage points when compared to the feature-fusion-based SVM model, and the overall relative improvement rate is 6.50%. The findings show that the fusion of leaf hyperspectral features and RGB image-derived phenotypic features, and the decision fusion of SVM and ResNet18, can be used to progressively improve the accuracy of identifying the growth year of cultivated ginseng. It offers a new approach to quality evaluation of cultivated ginseng and identifying its growth year.<\/jats:p>","DOI":"10.3389\/fpls.2026.1889841","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:36:06Z","timestamp":1789968966000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A growth-year classification model for cultivated ginseng based on feature\u2013decision-level fusion of leaf hyperspectral and RGB image features"],"prefix":"10.3389","volume":"17","author":[{"given":"Yifeng","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaozhong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology","place":["Changchun, China"]},{"name":"School of Data Science and Artificial Intelligence, Jilin Engineering Normal University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"An","sequence":"additional","affiliation":[{"name":"Education Quality Monitoring Center, Jilin Engineering Normal University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Data Science and Artificial Intelligence, Jilin Engineering Normal University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/S0169-7439(97)00029-4","article-title":"An introduction to wavelet transforms for chemometricians: A time-frequency approach","volume":"37","author":"Alsberg","year":"1997","journal-title":"Chemometrics Intell. 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