Agricultural big data for predicting crop water demand

Abstract

Agricultural Internet of Things (IoT) big data technology has begun to play an increasingly important role in agricultural production. In this study, the prediction of water demand for crop growth in agricultural tomato greenhouses at an angle of 25–35 degrees in Ningxia, China, is taken as the goal, and the corresponding agricultural IoT big data system is designed in detail, and the needed agricultural big data system is formed by real-time dynamic monitoring of environmental factors for crop growth in greenhouses, and then the correlation between water demand of crops in greenhouses and the growing environment and the stage of crop growth is explored and analyzed. And using K-MEANS, KNN, Random Forest algorithm to mine the generated big data, and finally scientifically predict the water demand for crop growth in agricultural tomato greenhouses at 25–35° angles in Ningxia, China, the results show that the research results effectively predict the water demand of crops in this type of agricultural greenhouses in the region, and provide a reference to the prediction of water demand for other crops in the similar greenhouses in the region, as well as to the Water saving prediction of crops in agricultural greenhouses in the region, rational planning of water resource utilization, and development of scientific and reasonable irrigation system for agricultural greenhouses are all important references.