The Research And Application Of Corn Pest Information Collection And Monitoring And Early Warning

Improving crop yield and quality is the requirement of modern agricultural development. Corn as one of the main food crops in China, its distribution range is wide, its branch is various. Under the background of global climate warming and the Crop variety constantly changing, corn pest population multiplies the evolution, corn pest population multiplies the evolution, and the occurrence rules of pests are always changing. The occurrence of pests seriously affects the yield and quality of corn. Therefore, to do prevention and control of insect pests of corn, are essential to guarantee the quality of the corn high yield high way. In order to manage corn pests effectively, we need to do a longterm monitoring of pest biological habits, summarizes the pests occurrence and succession law, optimize and promote the prevention and control technology, thus improve the prevention and control ability, which give full play of the role of pest prevention and control in the corn yield increase.The traditional manual collection and analysis method is time-consuming and inefficient. Because of lacking of timeliness and accuracy, it is difficult to meet the requirements of modern agricultural information collection. Agricultural Internet of things is formed by applying Internet of things technology and agricultural fields, and it is the specific application of Internet of things technology in agricultural production, business, and management. Sensing instrument is the core of the Internet of things. It uses all kinds of sensors to form the wireless sensor network(WSN), which will remotely transmit the information within the monitored region to the user terminal(e.g., agricultural production managers), real-time, quickly.This research with the help of the Internet of things technology, established a perfect pest data collection system, which can be collected digital information related to the corn pest damage, such as the weather, soil, and corn growing video. Analyzing the information can provide support for corn pest control.Support vector regression is a kind of machine learning algorithms for small sample, nonlinear. And its structural risk minimization principle can choose best between learning ability and generalization ability of the model, so as to improve the prediction ability of the model for unknown samples. This article uses the support vector regression machine to forecast the larvae plant rate of the first generation of corn borer, and get the occurrence degree of the first generation of corn borer according to classification standard of degree. Using the first generation of corn borer short-term larvae plant rate data and the corresponding meteorological data from Tai’an City, build a first generation of corn borer forecasting model and forecast the test set, and do back-substitution check in the training set. The experimental results show that the first generation of corn borer prediction model based on support vector regression can predict the first generation of the corn borer larvae plant rate and degree well, which provide guidance for the prediction of the first generation of corn borer early.