Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Ginseng is an important medicinal plant in Korea. The roots of the ginseng plant have medicinal properties; thus, it is very important to maintain the quality of ginseng roots. Root rot disease is a major disease that affects the quality of ginseng roots. It is important to predict this disease before it causes severe damage to the plants. Hence, there is a need for a non-destructive method to identify root rot disease in ginseng plants. In this paper, a method to identify the root rot disease by analyzing the RGB plant images using image processing and deep learning is proposed. Initially, plant segmentation is performed, and then the noise regions are removed in the plant images. These images are given as input to the proposed linear deep learning model to identify root rot disease in ginseng plants. Transfer learning models are also applied to these images. The performance of the proposed method is promising in identifying root rot disease.

Phenological studies are a prerequisite for accomplishing higher productivity and better crop quality in cultivated plants. However, there are no phenological studies on Panax ginseng that improve its production yield. This study aims to redefine the phenological growth stages of P. ginseng based on the existing Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale and proposes a disease control reference. This study was conducted at the Korea Ginseng Corporation Experiment Station in Gyeonggi province, South Korea. Phenological observations were performed once weekly or twice monthly, based on the developmental stages. The existing BBCH scale with a three-digit code was used to redefine and update P. ginseng’s phenological growth codes. The phenological description is divided into eight principal growth stages: three for vegetative growth (perennating bud, aerial shoot, and root development), four for reproductive growth (reproductive organ development, flowering, fruit development, and fruit maturation), and one for senescence according to the extended BBCH scale. A total of 58 secondary growth stages were described within the eight principal growth stages. Under each secondary growth stage, four mesostages are also taken into account, which contains the distinct patterns of the phenological characteristics in ginseng varieties and the process of transplanting seedlings. A practical management program for disease control was also proposed by using the BBCH code and the phenological data proposed in this work. The study introduces an extended BBCH scale for the phenological research of P. ginseng.