Spatial Information Extraction of Panax Notoginseng Fields Using Multi-algorithm and Multi-sample Strategy-Based Remote Sensing Techniques

Panax notoginseng has been regarded as one of raw materials in Chinese medicinal products which have been recommended as a common recommendation for the treatment of the novel coronavirus patients. Accordingly, in this study, a variety of supervised intelligent algorithms based on multi-algorithm and multi-sample strategy (MAMS) are used to implement spatial distribution information extraction for distinctive landscape types under unique environmental conditions. The experimental results demonstrate that the presented MAMS method is more effective than an individual classifier only with limited samples. The confidential interval of the planting area of Panax notoginseng is evaluated between 32.47 and 35.452 km, and the most likely area is determined nearby 34.042 km. Moreover, this presented study provides a practical foundation for land cover change detection under special observation conditions serving the government for a decision. There still needs further study, however, if this method could be applied to the fields of high-precision dynamic monitoring and change detection as a great contribution to precision agriculture.