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Geographic origins play a vital role in traditional Chinese medicinal materials. Using the geo-authentic crude drug can improve the curative effect. The main producing areas of Chinese wolfberry are Ningxia, Gansu, Qinghai, and so on. The geographic origin of Chinese wolfberry can affect its texture, shape, color, smell, nutrients, etc. However, the traditional method for identifying the geographic origin of Chinese wolfberries is still based on human eyes. To efficiently identify Chinese wolfberries from different origins, this paper presents an intelligent identification method for Chinese wolfberries based on color space transformation and texture morphological features. The first step is to prepare the Chinese wolfberry samples and collect the image data. Then the images are preprocessed, and the texture and morphology features of single wolfberry images are extracted. Finally, the random forest algorithm is employed to establish a model of the geographic origin of Chinese wolfberries. The proposed method can accurately predict the origin information of a single wolfberry image and has the advantages of low cost, fast recognition speed, high recognition accuracy, and no damage to the sample. [ABSTRACT FROM AUTHOR]

Background: Angelica sinensis (Oliv.) Diels (A. sinensis) is a Chinese herb grown in different geographical locations. It contains numerous active components with therapeutic value. Rhizosphere microbiomes affect various aspects of plant performance, such as nutrient acquisition, growth and development and plant diseases resistance. So far, few studies have investigated how the microbiome effects level of active components of A. sinensis. This study investigated whether changes in rhizosphere microbial communities and metabolites of A. sinensis vary with the soil microenvironment. Soils from the two main A. sinensis-producing areas, Gansu and Yunnan Province, were used to conduct pot experiments. The soil samples were divided into two parts, one part was sterilized and the other was unsterilized planting with the seedling variety of Gansu danggui 90–01. All seedlings were allowed to grow for 180 days. At the end of the experiment, radix A. sinensis were collected and used to characterize growth targets and chemical compositions. Rhizosphere soils were subjected to microbial analyses. Results: Changes in metabolic profiles and rhizosphere microbial communities of A. sinensis grown under different soil microenvironments were similar. The GN (Gansu non-sterilized), YN (Yunnan non-sterilized), GS (Gansu sterilized), and YS (Yunnan sterilized) groups were significantly separated. Notably, antagonistic bacteria such as Sphingomonas, Pseudomonas, Lysobacter, Pseudoxanthomonas, etc. were significantly (p < 0.05) enriched in Gansu soil compared with Yunnan soil. Moreover, senkyunolide I and ligustilide dimers which were enriched in GS group were strongly positively correlated with Pseudomonas parafulva; organic acids (including chlorogenic acid, dicaffeoylquinic acid and 5-feruloylquinic acid) and their ester coniferyl ferulate which were enriched in YS Group were positively associated with Gemmatimonadetes bacterium WY71 and Mucilaginibater sp., respectively. Conclusions: The soil microenvironment influences growth and level/type of active components in A. sinensis. Further studies should explore the functional features of quality-related bacteria, identify the key response genes and clarify the interactions between genes and soil environments. This will reveal the mechanisms that determine the quality formation of genuine A. sinensis. [ABSTRACT FROM AUTHOR]