Development and Testing of an Automated Solar-Powered Nutrient Film Technique (NFT) Hydroponic Planting System
Year: 2025
Type: article
Abstract: This paper presents an automated solar-powered Nutrient Film Technique (NFT) hydroponic system designed to address the major challenges of global food security and sustainable agriculture. With the global population projected to reach 9.3 billion by 2050, conventional agriculture is unlikely to meet the pressure - land, water, and energy are all in short supply and at a premium. The system proposed here is an intelligent integration of renewable solar energy and IoT-based automation with the objective of extracting maximum benefit from the available resources while providing better crop yields. There are three primary subsystems: an 80W solar power system with an MPPT charge controller and 12V batteries, an NFT hydroponic system constructed with 3″ PVC channels and a 12V DC nutrient pump, and an automation system created using an Arduino Nano microcontroller, an ESP8266 Wi-Fi module, NPK, and water level sensors. The system harvests data, uploads it to the Thingspeak IoT platform for real-time telemetry, command, and control services. In the field, performance trials showed that the system performed well, maintaining target nutrient levels (nitrogen: 100-130 ppm, phosphorus: 34-37 ppm, potassium: 168-173 ppm) while slashing water consumption by a whopping 90 percent compared with the existing technique. The use of solar energy and independence from the power grid, along with automatic management, avoided manual handling, resulting in accurate environmental conditions. The resultant watering and environmental changes were done as scheduled, ensuring the maintenance of stable plant health. Overall, this study provides a practical and energy-saving approach for urban agriculture, especially for those land-limited and uneven-power-supplied regions. There is plenty of room for further research and development, AI optimization, new types of energy storage, and full-blown cost estimates could help scalability.
DOI: 10.33003/ztxky953
Language: English
Subfield: Aquatic Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7166532779