Development of IoT System and Weather Database for six Forestry Research and Student Training Stations

Abstract

Various forest research and student training stations, operating under the faculty of forestry, have been established across the country, comprising a total of eight stations. These stations play a crucial role in education and research, including ecology and forest plantation planning etc. In order to ensure the accuracy of meteorological information, researchers are required to reference data from nearby weather stations, which means that the data obtained do not represent the station’s actual measurements. Therefore, a conceptual framework for this study was developed, with the objectives to develop weather monitoring sensors and an internet of things (IoT) based data transmission system. This would help in the development of a weather measurement database system and with the ability to evaluate the accuracy of the IoT based weather monitoring against the standard instruments at the Had Wanakorn forestry research and student training stations. The IoT sensors would operate in conjunction with the NETPIE cloud server, which serves as an intermediary for transmitting data to the databases and display the information on a web interface. The interface is divided into three modules: general users, who can view weather information; members, who can download weather data; and administrators, who can add, edit or delete both member accounts and station data.

The calibration study between the IoT–based weather monitoring device and the standard instrument model WS–GP1 DELTA–T DEVICE at the Had Wanakorn forestry research and student training stations indicated that, on average, the IoT device exhibited a root mean square error (RMSE) of 5.6 % and the system achieved measurement accuracies of 93.9% for temperature, 91.9% for relative humidity, 96.1% for light intensity, 93.3% for wind speed, and 94.6% for PM2.5 dust concentration.