IoT-based automation for hydroponics — nutrient monitoring, pH control, and environmental sensors (2 citations).
Hydroponics requires precise monitoring of nutrient solution pH, electrical conductivity (EC), and temperature for optimal plant growth. Manual monitoring is labor-intensive and prone to inconsistency, particularly for small-scale operations in Indonesia where labor costs challenge profitability.
Automated systems exist but are often designed for commercial scale with costs prohibitive for Indonesian smallholders. Off-the-shelf solutions require significant capital and technical expertise for installation and maintenance that rural farming communities lack.
The gap between low-cost manual methods and expensive commercial automation leaves a need for accessible IoT-based systems that can maintain precision nutrient parameters without constant human intervention, using components available in the Indonesian market.
The system integrates pH and EC sensors with an ESP32 microcontroller for continuous nutrient monitoring and automated adjustment. Sigit contributed to sensor integration and control algorithm development. The automation maintains pH within the 5.5–6.5 range and EC at crop-appropriate levels through periodic nutrient dosing.
Environmental sensors for temperature and humidity control ventilation and shading systems. The ESP32 communicates via Wi-Fi using MQTT protocol to a cloud dashboard, enabling remote monitoring and alerting. Actuators include peristaltic pumps for precise nutrient dosing, water circulation pumps, and solenoid valves for flow control.
The control loop operates on a configurable interval: sensors read current conditions, the ESP32 compares against user-defined setpoints, and if values drift outside acceptable ranges, actuators are triggered to correct the nutrient solution. All readings are logged for trend analysis and crop optimization.
The automated system maintained nutrient parameters within target ranges 92% of the time versus 65% for manual management. Crop yields increased 20–25% with improved consistency across harvest cycles. Labor requirements for nutrient management decreased by approximately 70%, freeing farmers for other productive tasks.
Lettuce grown under automated monitoring showed more uniform growth rates and higher average weight at harvest. The consistency improvement was most significant during hot periods when manual monitoring frequency typically drops due to discomfort working in greenhouse conditions.
The study deployed sensor arrays across multiple NFT (Nutrient Film Technique) channels, measuring pH with glass-electrode probes calibrated daily, electrical conductivity with two-electrode sensors, and temperature with DS18B20 digital sensors at 0.5°C accuracy.
Control algorithms used hysteresis-based threshold triggering to prevent actuator oscillation. Nutrient dosing was performed by peristaltic pumps with calibrated flow rates of 1.5 mL/s, enabling precise adjustment of solution concentration. The ESP32 logged all readings to a Firebase Realtime Database via MQTT with 60-second reporting intervals.
The experimental setup compared two identical NFT systems: one with full automation and one with manual monitoring by farm workers following standard operating procedures. Both systems grew the same lettuce variety (Lactuca sativa) under identical greenhouse conditions for an 8-week growth cycle. Plant height was measured weekly as the primary growth metric.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Indonesian-language publication
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