Construction Of Wireless Monitoring And Prediction Model For Key Field Parameters Of Panax Notoginseng Growth Cycl

With the continuous development of Internet of things technology and the continuous enrichment of data information processing means,it is more and more convenient to obtain data,which promotes the further practical application of data information processing.Panax notoginseng is a characteristic medicinal cash crop in Yunnan Province with strict requirements for the growth environment.Its yield and quality are closely related to the environment in the growth period.Combined with Internet of things technology,machine learning algorithm and monitoring visualization,this paper provides wireless monitoring and prediction model of Panax notoginseng growth for key field parameters during the growth process of Panax notoginseng,so as to play an escort role in the growth and cultivation process of Panax notoginseng.The main work of this paper is summarized as follows:(1)Combined with Internet of things technology,a soil temperature,humidity and conductivity detection system is designed.The system includes host device,slave device and upper computer.The slave device is used to collect the temperature,humidity and conductivity information at each distribution point in the field of Panax notoginseng greenhouse,and the host device is used to transmit the received information collected by the slave device to the upper computer,so that the data can be stored and managed in the upper computer,which is convenient for further analysis and application of soil data in Panax notoginseng greenhouse.(2)The prediction model of Panax notoginseng leaf area growth is constructed based on particle swarm optimization random forest algorithm.The model uses the data of meteorological factors in the shed of Panax notoginseng planting base in Luxi County,Honghe Autonomous Prefecture,Yunnan Province from April to October 2018 and the data of Panax notoginseng leaf area growth as the training set and test set.The experimental results show that the prediction model of Panax notoginseng leaf area growth constructed by this algorithm has high prediction accuracy.(3)The prediction model of plant height growth of Panax notoginseng is constructed based on gray wolf random forest algorithm.Firstly,the soil meteorological factor data and the plant height growth data of Panax notoginseng in the plastic greenhouse of the water-saving irrigation experimental base in Chenggong campus of Kunming University of technology from April to July 2021 are collected by using the soil temperature,humidity and conductivity detection system,and then the collected data are used as the training set and test set to construct the plant height growth prediction model of Panax notoginseng.The experimental results show that the height growth prediction model of Panax notoginseng constructed by this algorithm has high prediction accuracy.(4)Visual design of soil meteorological factors.Firstly,the soil meteorological factor data of Panax notoginseng field are collected through the detection system,and the collected data are cleaned and analyzed.Then,the soil humidity data are interpolated and filled by Kriging interpolation method,and the interpolated soil humidity field is visually designed,so that the key soil parameter factors affecting the growth of Panax notoginseng are presented in the form of multi-dimensional images.It provides effective technical support for later management and decision-making.