Fuzzy-Based Smart Farming and Consumed Energy Comparison Using the Internet of Things

Abstract

Hydroponics is a farming method that makes efficient use of both space and land. Growing plants hydroponically requires consideration of pH, nutrition, water regulation, and light sources. The latter two can be managed using pumps and light-emitting diodes (LEDs), respectively; which require electrical energy. This research investigated hydroponics and electrical energy consumption concerns for prototype design, implementation, testing, and analysis with a framework of fuzzy logic and the Internet of Things (IoT). This work employed BH1750, SEN0244 TDS, PH-4502C, ACS712, and 170640 sensors for temperature, illuminance, nutrition, pH, electric current, and voltage sensing, respectively. The control parts were an Arduino Mega 2560 microcontroller board, ESP8266 NodeMCU, and DS3231 RTC, and the output parts were the growing light LEDs, LCD, DC water, and peristaltic pumps. Swamp cabbage plant samples were utilized for three comparative prototypes: fuzzy-based, schedule-based, and natural methods. The testing was conducted for 36 days. The results showed that the typical plant height difference between the fuzzy-based and natural methods was 1.75 cm (26.3%) and that of the schedule-based and natural methods was 1.28 cm (22.8%). Furthermore, the typical plant growth rates were 0.50 cm/day, 0.44 cm/day, and 0.32 cm/day for the fuzzy-based, schedule-based, and natural methods, respectively. Moreover, consumed energy savings with the fuzzy-based versus schedule-based methods was 49.11 Wh (4.75%), 49.02 Wh (4.75%), or 48.99 Wh (4.74%) using ordinary, Simpson’s composite rule, or trapezoidal composite rule computation methods, respectively. The fuzzy-based method undoubtedly increased the plant’s height and growth rate, while requiring energy consumption that was less than that of the schedule-based method.

Keywords

• Energy
• fuzzy
• hydroponics
• illuminance
• sensor