Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Abstract

Smart farms are a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as greenhouses. Various sensors are installed in greenhouses to collect data, which AI analyzes to find the optimal crop growth environment. Various studies have been conducted to control this environment automatically. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is tough to collect sensor data to monitor the growth environment of crops in open fields where weather conditions change significantly and the power supply is complex. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.