High-School CanSat Model for Advancement of Agricultural Process in Thailand - Bhavat NGAMDEEVILAISAK; Chayakorn VONGBUNSIN; Chayanin UTHANPATHUMROS; Vivatsathorn Thitasirivit; Phachara Phumiprathet; Peeramed Chodkaveekityada (TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES AEROSPACE TECHNOLOGY JAPAN, 2021)
Thailand's agriculture is economically remarkable but most of agriculturists and farmers, on the contrary, have significantly low profit. Nowadays, technology is used to solve many agricultural problems, especially analyze area to find the most suitable crops and area for efficient growth rate of crops. Agricultural Exploration Assistant Satellite or AEASat is the CanSat prototype that participated in Thailand CanSat Competition 2018 and received The First Place Winner and The Best Presentation Awards. The goals of AEASat are to study environmental growth factors (temperature, relative humidity, average rainfall, carbon dioxide intensity, red and blue light intensity) of six Thai economic crops (rice, cassava, maize, sugarcane, rubber tree, palm) and find the most suitable area for many agricultural actions. The first mission of AEASat takes place in Chai Badan, Lop Buri Province, Thailand. Electronic structure of AEASat is categorized into four floors respect to their functions: sensors floor, main controller floor, data logger and wireless communication floor, and power management floor. Another part of the mission is the ground unit. The ground unit also collects environmental factors including light intensity which AEASat does not collect. The last part of the mission is ground station. The function of the ground station is receiving data from AEASat with wireless 488-MHz-frequency LoRa module. The ground station is also used to track AEASat from deployment to a touchdown. Qualitative data and quantitative data are analyzed with different methods. The qualitative data is evaluated to find the growing suitability in the area by using Geographic Information System (GIS) software. Aerial photograph, a part of qualitative data, is analyzed by visual interpretation. The quantitative data is analyzed by calculating average of the correlation coefficients of six factors by using MATLAB. It is calculated in order to find the most suitable crop out of six to grow in the area. As a result, sugarcane is the most suitable crop to grow in Chai Badan area. Agriculturists and farmers can have access to website which the results, detail, and description of the exploration are uploaded. In conclusion, if agriculturists and farmers grow the crops as advised, they can expect more products quantity.
Keywords: Digital Transformation in Industry; Advanced Manufacturing and Logistics Optimization; Strategic Planning and Analysis