This research presents an improved and more effective approach for data acquisition of recirculation aquaculture system (RAS). The previous research, the system uses manual methods to take the important data from RAS and it wastes the time and also gets late response from the fish farmer if the data is not in the good condition. As a result, fog computing technology is applied to overcome all these problems and acts as advance data acquisition system to keep data safely by sharing the processed data in fog computing for every tanks and analyze the data to make an accurate control/decision in the real time. Besides, open source technology plus embedded system based will be integrated for this research because its benefits such as small size, low cost, light weight, portable, high efficiency and low power consumption. This research has achieved the objectives which are design a data collecting system for RAS, design a data processing system using fog computing and integrate, test and validate automatic data collection and processing strategy for recirculation aquaculture system (RAS). The data collecting system for RAS, RaspDAQ is developed by connecting Raspberry Pi 3 to temperature sensor (LM35DT) using analogue digital converter (ADC) MCP3002, water level sensor (HC-SR04), Rpi camera module, LEDs and buzzer. Software and program are built using Python and Apache server to run every functions of RaspDAQ. While third Raspberry Pi 3 is setup as data processing system, RaspFog using PHP, Apache and MySQL server. Both RaspDAQ and RaspFog are based on Raspbian operating system. After that, RaspDAQ1 and RaspDAQ2 are connected to RaspFog using WiFi technology to send sensors data in real time. The received data are stored and plotted using Highcharts.com graph. The data collecting system, RaspDAQ and data server and processor, RaspFog has been tested and validated. At the same time, users can see the graph output in the real time for temperature, water level sensor and real condition using Rpi camera module of RaspDAQ1 and RaspDAQ2 by browsing RaspFog website. From the observation, data has been transferred from RaspDAQ to RaspFog in a short duration which is less than 15 seconds. Consequently, the efficiency of data acquisition process has been improved from manual system to fog computing technology successfully.