A growth-year classification model for cultivated ginseng based on feature-decision-level fusion of leaf hyperspectral and RGB image features.

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.