Smart Agricultural Big Data Preprocessing Method Based on Adaptive Compression Algorithm

Abstract

The application of agricultural Internet of Things technology is to install a variety of sensor parts and control actuators in the greenhouse, which compose the dropper system, temperature change system, humidification system and ventilation system, etc. The data transmission is completed through wireless network sensors to conduct real-time remote monitoring of crop environmental parameters. However, the network transmission of wireless sensor network is quite energy consuming, which easily leads to the premature death of nodes. The main purpose of this paper is to reduce the amount of data transmitted by wireless sensors to decrease the energy consumption of nodes, thus put forward smart agricultural big data preprocessing method based on adaptive compression algorithm. Firstly, the increase or decrease of sampling frequency is judged by calculating the average variation of collected data in a period of time in the historical data. Then, the optimal solution of sampling is searched by heuristic algorithm. By building a big data platform and adjusting the sampling frequency adaptatively, the purpose of reducing the amount of data in the processing process and not losing the information is achieved. This method can alleviate the burden of network and information processing system and reduce the time of data cleaning and prediction in later stage.