Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures.

Medicinal plants produce important substrates for their adaptation and defenses against environmental factors and, at the same time, are used for traditional medicine and industrial additives. Plants have relatively little in the way of secondary metabolites via biosynthesis. Recently, the whole-genome sequencing of medicinal plants and the identification of secondary metabolite production were revolutionized by the rapid development and cheap cost of sequencing technology. Advances in functional genomics, such as transcriptomics, proteomics, and metabolomics, pave the way for discoveries in secondary metabolites and related key genes. The multi-omics approaches can offer tremendous insight into the variety, distribution, and development of biosynthetic gene clusters (BGCs). Although many reviews have reported on the plant and medicinal plant genome, chemistry, and pharmacology, there is no review giving a comprehensive report about the medicinal plant genome and multi-omics approaches to study the biosynthesis pathway of secondary metabolites. Here, we introduce the medicinal plant genome and the application of multi-omics tools for identifying genes related to the biosynthesis pathway of secondary metabolites. Moreover, we explore comparative genomics and polyploidy for gene family analysis in medicinal plants. This study promotes medicinal plant genomics, which contributes to the biosynthesis and screening of plant substrates and plant-based drugs and prompts the research efficiency of traditional medicine.

Hypertension is a cardiovascular disease that causes great harm to health and life, affecting the function of important organs and accompanied by a variety of secondary diseases, which need to be treated with drugs for a long time. P. ternata alone or combination with western medicine has played an important role in traditional Chinese medicine. Although P. ternata is used clinically to treat hypertension, its functional molecular mechanism and pharmacological mechanism have not been elucidated. Therefore, in this study, the potentially effective components, and targets of P. ternata in the treatment of hypertension were screened by the method of network pharmacology, and the mechanism of P. ternata in the treatment of hypertension was analyzed by constructing a component-target relationship network, PPI interaction network, targets’ function analysis, and molecular docking. In the study, 12 potentially effective components and 88 targets were screened, and 3 potential protein modules were found and analyzed after constructing a PPI network using targets. In addition, 10 targets were selected as core targets of the PPI network. After that, the targets were analyzed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the molecular docking method is used to study the interaction between the targets and the active components. The above evidence shows that the mechanism of P. ternata in the treatment of hypertension is complicated, as it acts in many ways, mainly by affecting nerve signal transmission, cell proliferation, and apoptosis, calcium channels, and so on. The binding between targets and active components mainly depends on Pi bonds and hydrogen bonds. Using the method of network pharmacology and molecular docking to analyze the mechanism of P. ternata in the treatment of hypertension will help to provide a better scientific basis for the combined use of traditional Chinese medicine and western medicine, and will better help to improve the quality of P. ternata and point out its direction.

Soil types and cropping systems influence the diversity and composition of the rhizospheric microbial communities. Coptis chinensis Franch is one of the most important medicinal plants in China. In the current study, we provide detailed information regarding the diversity and composition of rhizospheric fungal communities of the C. chinensis plants in continuous cropping fields and fallow fields in two seasons (winter and summer), using next-generation sequencing. Alpha diversity was higher in the five-year C. chinensis field and lower in fallow fields. Significant differences analysis confirmed more fungi in the cultivated field soil than in fallow fields. Additionally, PCoA of beta diversity indices revealed that samples associated with the cultivated fields and fallow fields in different seasons were separated. Five fungal phyla (Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota and Mucoromycota) were identified from the soil samples in addition to the unclassified fungal taxa and Cryptomycota, and among these phyla, Ascomycota was predominantly found. FUNGuild fungal functional prediction revealed that saprotroph was the dominant trophic type in all two time-series soil samples. Redundancy analysis (RDA) of the dominant phyla data and soil physiochemical properties revealed the variations in fungal community structure in the soil samples. Knowledge from the present study could provide a valuable reference for solving the continuous cropping problems and promote the sustainable development of the C. chinensis industry.

Chinese goldthread (Coptis chinensis Franch.) represents one of the most important medicinal plants with diverse medicinal applications, but it easily suffers from continuous cropping obstacles in the plantation. In this study, we have selected eight different continuously cropped fields with C. chinensis and fallow field, providing detailed information regarding the diversity and composition of the rhizospheric bacterial communities. We have found a significant difference between fallow field (LH) and other continuously cropped fields in soil pH; the total content of nitrogen, phosphorus, and potassium; and soil enzyme activities. The results indicate that continuous cropping had a significant effect on soil physicochemical properties and enzyme activities under different plant cultivations. The relative abundance of bacterial phyla was significantly altered among the fields; for example, proteobacteria and Actinobacteria were observed to be higher in continuous cropping of maize (HY6) and lower in sweet potato continuous cropping (HH). Alpha diversity analysis showed that different plants with different years of continuous cropping could change the diversity of bacterial communities, among which the effect of maize and Polygonum multiflorum continuous cropping were most significant. Principle coordinate analysis (PCoA) showed that continuously cropped C. chinensis (LZ) and cabbage continuously cropped for 2 years (HS) were slightly clustered together and separated from LH and others. The results showed that the similarity of the bacterial community in the same crop rotation was higher, which further indicated that the bacterial community structure was significantly altered by the continuous cropping system and plant species. Our study provides a foundation for future agricultural research to improve microbial activity and increase crops/cash-crops productivity under a continuous cropping system and mitigate continuous cropping obstacles.

Soil microorganisms are critical factors of plant productivity in terrestrial ecosystems. Coptis chinensis Franch is one of the most important medicinal plants in China. Soil types and cropping systems influence the diversity and composition of the rhizospheric microbial communities. In the current study, we provide detailed information regarding the diversity and composition of the rhizospheric bacterial communities of the C. chinensis plants in continuously cropped fields and fallow fields in two seasons (i.e., winter and summer) using next-generation sequencing. The alpha diversity was higher in the five-year cultivated C. chinensis field (CyS5) and lower in fallow fields (NCS). Significant differences analysis confirmed more biomarkers in the cultivated field soil than in fallow fields. Additionally, the principal coordinate analysis (PcoA) of the beta diversity indices revealed that samples associated with the cultivated fields and fallow fields in different seasons were separated. Besides, Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Gemmatimonadetes were the top bacterial phyla. Among these phyla, Proteobacteria were found predominantly and showed a decreasing trend with the continuous cropping of C. chinensis. A phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) revealed that the abundance of C and N functional genes had a significant difference between the soil samples from cultivated (CyS1, CyS3, and CyS5) and fallow (NCS) fields in two seasons (winter and summer). The principal coordinate analysis (PCoA) based on UniFrac distances (i.e., unweighted and weighted) revealed the variations in bacterial community structures in the soil samples. This study could provide a reference for solving the increasingly severe cropping obstacles and promote the sustainable development of the C. chinensis industry.