Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Abstract

Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments.