Yield Evaluation of Brassica rapa, Lactuca sativa, and Brassica integrifolia Using Image Processing in an IoT-Based Aquaponics with Temperature-Controlled Greenhouse

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Abstract
The paper introduced the development of a self-sustainable smart aquaponics system in a temperature-controlled greenhouse with a monitoring and automatic correction system using an Android device through the Internet of Things (IoT) and plant growth monitoring system through image processing using Raspberry Pi. The system involves the acquiring of real-time data detected by the light intensity sensor, and air temperature and humidity sensor. It also includes the monitoring of the pH level and temperature of the recirculating water of the system. If the acquired data is not within the threshold range, the correcting devices, namely grow lights, exhaust and inlet fans, evaporative cooler, aerator, and peristaltic buffer device were automatically triggered by the system to correct and achieve its normal status. The internet remote access includes the effective wireless transmission and reception of data reports between the system and an Android unit with the Android application in real-time. The study focused on the evaluation of two experimental set-ups comparing the plant growth between conventional soil-based farming and the smart aquaponics system using image processing. After data gathering, results showed that the smart aquaponics set-up successfully produced a yield better than the conventional farming set-up.