The sustainable use of medicinal plants is the foundation of the inheritance of traditional Chinese medicine(TCM) and the acquisition of information on medicinal plants is the basis for the development of TCM. The traditional methods of investigating medicinal plant resources are disadvantageous in strong subjectivity and poor timeliness, making it difficult to real-time monitor medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants. The application of this technology has made up for the shortcomings of traditional methods. The open-access remote sensing data with medium spatial resolution satellites provide an opportunity for extracting information on medicinal plant resources. This study firstly introduced the principles of remote sensing technology, summarized the satellites and the parameters commonly used in the field of medicinal plant resources, and compared the survey methods of remote sensing technology with traditional methods. Secondly, it reviewed the applications of remote sensing technology in the extraction of information on the cultivation of medicinal plants and the common methods for extracting the planting structure information of medicinal plants based on remote sensing technology. Thirdly, the applications of remote sensing technology in the investigation and monitoring of medicinal plants were further analyzed with the research objects divided into wild and cultivated medicinal plants according to the characteristics of the habitats. Finally, it pointed out the key unsolved technical problems in the remote sensing monitoring of medicinal plant resources, and proposed solutions for the intelligent information processing of medicinal plants based on remote sensing big data, which is expected to provide references for the development of remote sensing technology in derivative application in medicinal plant resources.
Medicine, Chinese Traditional ; Plants, Medicinal ; Remote Sensing Technology

Identification of Chinese medicinal materials is a fundamental part and an important premise of the modern Chinese medicinal materials industry. As for the traditional Chinese medicinal materials that imitate wild cultivation, due to their scattered, irregular, and fine-grained planting characteristics, the fine classification using traditional classification methods is not accurate. Therefore, a deep convolution neural network model is used for imitating wild planting. Identification of Chinese herbal medicines. This study takes Lonicera japonica remote sensing recognition as an example, and proposes a method for fine classification of L. japonica based on a deep convolutional neural network model. The GoogLeNet network model is used to learn a large number of training samples to extract L. japonica characteristics from drone remote sensing images. Parameters, further optimize the network structure, and obtain a L. japonica recognition model. The research results show that the deep convolutional neural network based on GoogLeNet can effectively extract the L. japonica information that is relatively fragmented in the image, and realize the fine classification of L. japonica. After training and optimization, the overall classification accuracy of L. japonica can reach 97.5%, and total area accuracy is 94.6%, which can provide a reference for the application of deep convolutional neural network method in remote sensing classification of Chinese medicinal materials.
Lonicera ; Neural Networks, Computer ; Remote Sensing Technology

Unmanned aerial vehicle(UAV) remote sensing and vegetation index have great potential in the field of Chinese herbal medicine planting. In this study, the visible light image of Polygonatum odoratum planting area in Changyi district of Jilin province were acquired by UAV, and the real-time monitoring of P. odoratum planting area was realized. The green leaf index(GLI) was established, and GLI values of P. odoratum were collected used the spatial sampling points. To compare the GLI values in different periods, it was found that the GLI values of P. odoratum have three stages changing rule of rising-gentle-falling related to the germination, vigorous growth and withered of P. odoratum growth. Meanwhile, the GLI values were compared with four biomass data of P. odoratum, including plant height, leaf area, chlorophyll a and chlorophyll b content in leaves, and it was found that the GLI value was related to the growth potential of P. odoratum. The GLI value with a rapid increase in rising stage or at a high level in the gentle stage means the P. odoratum was in a better growth potential. GLI value has a same change trend with plant height, and has certain correlation with plant height and leaf area. However, there is no obvious relationship between chlorophyll a and chlorophyll b contents in leaves and GLI value. The study clarified the change rule of GLI value of P. odoratum, explained the reason for the change of GLI value, and expanded the application range of GLI. The research shows that UAV and vegetation index can be applied to monitoring the Chinese herbal medicines planting, and provides a new idea for exploring more effective information extraction methods of Chinese herbal medicines.
Chlorophyll A ; Plant Leaves ; Polygonatum ; Remote Sensing Technology

OBJECTIVES: The Internet of Things (IoT) and its applications are growing simultaneously. These applications need new intelligent devices along heterogeneous networking. Which makes them costly to implement indeed. Platforms and open devices designed for open-source hardware are possible solutions. This research was conducted under an IoT design, implementation, and assessment model for the remote monitoring of pulse oximetry via oxygen partial saturation (SpO2) and heart rate (HR) with low-energy consumption. METHODS: This study focused on the development of SpO2 and HR measurements that will allow the monitoring and estimation in real time of the user's state and health related to the established parameters. Measurements were acquired and recorded using a remote web server that recorded the acquired variables for further processing. The statistical analysis data allows comparison of the registered data measured with theoretical models. RESULTS: The IoT model was developed use Bluetooth low-energy devices, which comply with low-cost and open-hardware solutions operated via ‘HTTP requests’ for data transmission and reception from a cloud server to an edge device. Network performance assessment was conducted to guarantee the availability and integrity of the acquired values and signals. The system measured SpO2 and HR variables. The most significant result was to achieve energy consumption 20% lower than that of devices in the market. CONCLUSIONS: In summary, the acquired data validation based on the IoT model had a transmission error of 0.001% which proves its applicability in healthcare.
Delivery of Health Care ; Heart Rate ; Internet ; Models, Theoretical ; Monitoring, Physiologic ; Oximetry ; Oxygen ; Remote Sensing Technology

In order to solve the problem of manual area measurement,the traditional methods of medicinal planting area statistics are difficult to meet the needs of rapid area survey application. This paper uses the UAV remote sensing method with the advantages of unmanned,automatic,high efficiency,high score and short production cycle to monitor the shape of Callicarpa nudiflora. A solution for aerial photography,image data acquisition and data processing of drones were designed for characteristics and planting conditions. After data processing and statistical analysis,detailed information on the location and area of the C. nudiflora in the target area was obtained. Then the accuracy comparison analysis was carried out with the measured results of the C. nudiflora. The results show that the UAV is feasible for the monitoring of C. nudiflora,and has a good application prospect in the monitoring of Chinese herbal medicine planting.
Callicarpa ; Photography ; Plants, Medicinal ; Remote Sensing Technology

The dried roots of Panax ginseng are used as medicines. In this paper,multi-time satellite sensing image data are used for image registration by radiometric correction,atmospheric pressure correction,the data of different years were compared. The multiscale segmentation of the sensing image was successively carried out by using object-oriented method. Combining with the characteristics of the sensing image participated in the field survey,the objective was to understand the speckles of the environmental parameters distribution map of Changbai county in 2017 and 2018. The parameter area of Changbai county was calculated by using GIS spatial analysis tools. The union,erase and intersect tools of " analysis to OLS" overlay in " Arc Toolbox" were used to analyze the parametric area of Changbai county from 2017 to 2018. The results showed that the parameter area of Changbai county in 2017 was 27 400 mu( 1 mu≈667 m²),and the parameter area in 2018 was 13 900 mu. The parameter area of the new park in Changbai County in 2018 was 12 500 mu,and the harvested area in 2017 was 27 000 mu. Through the analysis and study of the regional change of the park participating in the training area,it has significance for guiding the park participating in the actual production planning and layout in Changbai county in the next step.
Gardens ; Panax ; Remote Sensing Technology

The study is aimed to effectively obtain the planting area of traditional Chinese medicine resources. The herbs used as the material for traditional Chinese medicine are mostly planted in natural environment suitable mountainous areas. The UAV low altitude remote sensing data were used as the samples and the GF-2 remote sensing images were applied for the data source to extract the planting area of Salvia miltiorrhiza and Artemisia argyi in Luoning county combined with field investigation. Remote sensing satellite data of standard processing obtain specific remote sensing data coverage. The UAV data were pre-processed to visually interpret the species and distribution of traditional Chinese medicine resources in the sample quadrat. Support vector machine( SVM) was used to classify and estimate the area of traditional Chinese medicine resources in Luoning county,confusion matrix was used to determine the accuracy of spatial distribution of traditional Chinese medicine resources. The result showed that the application of UAV of low altitude remote sensing technology and remote sensing image of satellite in the extraction of S. miltiorrhiza and other varieties planting area was feasible,it also provides a scientific reference for poverty alleviation policies of the traditional Chinese medicine Industry in local areas.Meanwhile,research on remote sensing classification of Chinese medicinal materials based on multi-source and multi-phase high-resolution remote sensing images is actively carried out to explore more effective methods for information extraction of Chinese medicinal materials.
Altitude ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional ; Natural Resources ; Remote Sensing Technology ; Support Vector Machine

In order to comprehensively monitor the dynamic change of Paeonia lactiflora planting area,the investigation of P. lactiflora planting area in Dangshan was carried out. It can provide reference for the planting detection of P. lactiflora in Huaibei Plain.Based on remote sensing technology,this paper extracts the planting area of P. lactiflora in Dangshan in 2018 by using the minimum distance method,maximum likelihood method,parallel hexahedron method and Mahalanobis distance method,using the remote sensing image of ZY-3 Satellite as the data source,and makes a comparative analysis with the results. The results show that the maximum likelihood method is better than the other three methods. This method can provide reference for remote sensing monitoring of P. lactiflora planting area in China.
China ; Paeonia ; Remote Sensing Technology

Moutan Cortex is one kind of famous medicinal materials. The dry root bark of Paeonia ostii which is a genuine medicinal material produced in Tongling,Anhui province,and later was introduced to Heze,Shandong province and Bozhou,Anhui province.Dangshan county is located at the northern end of Anhui province and adjacent to Shandong province. Its medicinal seedlings were came from Heze,Shandong province. At present,there is a lack of scientific investigation on the planting area of P. ostii in north China plain. On the basis of field investigation and remote sensing technology,through the data source provided by the remote sensing image of " Resources 3"( ZY-3),combined with the biological characteristics of P. ostii,the planting area of P. ostii in Dangshan county was extracted by field investigation and supervisory classification. The supervise classification method with the highest interpretation accuracy so far,the overall accuracy was 97. 81%,Kappa coefficient 0. 96. The results showed that the remote sensing classification method based on the maximum likelihood classification could extract P. ostii plots in the study area effectively. This study provides a scientific basis for the protection and rational utilization of traditional Chinese medicine resources,the development policy of traditional Chinese medicine industry and the long-term development plan in Dangshan county,and provides technical support for the poverty alleviation of traditional Chinese medicine industry in Dangshan county. It provides scientific reference for the application of remote sensing technology to investigate the planting area of P. ostii in in north China plain.
China ; Medicine, Chinese Traditional ; Paeonia ; Remote Sensing Technology

Due to the large amount of Codonopsis pilosula planted in Weiyuan county,and the arable land area,the local medicinal materials office uses a large amount of manpower,financial resources and material resources to estimate its area every year. In order to extract the information of local Chinese medicinal materials more quickly and simply,we try to apply remote sensing technology to the extraction of Chinese medicinal materials. This paper will use Weiyuan county of Gansu province as the research area,and use the domestic ZY-3 Satellite multi-spectral remote sensing image as the data source to find out the spectral characteristics of the party's participation in other remote sensing images. The visual interpretation method was used to extract the planting area of the C. pilosula in Weiyuan county. The estimated value of the planting area of C. pilosula using satellite remote sensing technology was 75 965 mu( 1 mu≈667 m²),which was basically consistent with the field survey data of the local medicinal materials office. After the accuracy verification,it was found that the precision of C. pilosula planted by visual interpretation was more than 70%. It is concluded that the satellite remote sensing technology can be used to extract the information of C. pilosula and it can provide the relevant information of the planting area of Chinese medicinal materials quickly and accurately.
China ; Codonopsis ; Plants, Medicinal ; Remote Sensing Technology