Prediction model of disease and pest epidemic of jujube based on Internet of Things

To reduce the loss of the jujube production and develop more effective pest control measures, this paper studies the prediction model of jujube pest epidemic based on the Internet of Things technology. The data of jujube growth was collected based on the Internet of Things technology, and multi-source information was obtained by combining sensor technology and network communication technology. The characteristic bands of the jujube image were extracted. The useless bands were removed, and the overall physical information of the image was retained. The prediction model of the jujube pest and disease epidemic situation was established, and the prediction factors with poor stability were processed by BP neural network structure to realize the prediction. Through experimental demonstration and analysis, compared with the actual occurrence degree of diseases and insect pests, the prediction curve of the occurrence degree of diseases and insect pests of jujube by this model is consistent with the changing trend of the actual occurrence degree curve. Compared with the traditional model, the prediction error of the proposed model is smaller, which proves that the proposed model is more effective. In this paper, the model can obtain effective information on the basis of multi-source data, and get more accurate prediction results, which provides a scientific basis for decision-making for the formulation of pest and disease control plan to ensure the high and stable yield of jujube.