NFT Hydroponic Control Using Mamdani Fuzzy Inference System (2208.00364v1)

Abstract: The Nutrient Film Technique (NFT) method is one of the most popular hydroponic cultivation methods. This method has advantages such as easier maintenance, faster and optimal plant growth, better use of fertilizers, and less deposition. The disadvantages of NFT include the consumption of electrical power and the faster spread of disease. Therefore, NFT requires a good nutrient control and monitoring system to save electricity and achieve optimal growth and resistance to pests and diseases. In this study, a nutrient control was designed with indicators of pH and TDS levels and equipped with an Internet of Things (IoT) based monitoring system. The control system used is the Mamdani Fuzzy Inference System. The output of the system is the active time of the pH Up, pH Down, and AB Mix nutrient pumps, which aim to normalize the pH and TDS of nutrient liquids. The experimental results show that one to three control steps are needed to normalize pH. One control step has a response time of 60 seconds, and it can prevent pH Up and pH Down oscillations. As for TDS control, the prediction of AB mix pump active time works accurately, and TDS levels can be normalized in one control step. Overall, based on surface control, simulations, and real experimental data, it is indicated that the control system operates very well and can normalize pH and TDS to the desired normal standard.

• The paper demonstrates a Mamdani fuzzy inference system that normalizes pH within one to three control steps, ensuring stable nutrient levels.
• It employs an IoT framework for real-time monitoring, enabling precise adjustments to pH and TDS in NFT hydroponics.
• Experimental results validate accurate TDS control and prevention of oscillations, highlighting the system's practical efficiency.

The paper "NFT Hydroponic Control Using Mamdani Fuzzy Inference System" addresses the challenges of maintaining optimal nutrient levels in the Nutrient Film Technique (NFT) method of hydroponic cultivation. While NFT is recognized for its benefits such as easier maintenance, rapid plant growth, optimal fertilizer use, and minimal nutrient deposition, it also poses challenges like high electrical power consumption and susceptibility to rapid disease spread. To mitigate these issues, a robust nutrient control and monitoring system is necessary.

The researchers implemented a control system using the Mamdani Fuzzy Inference System. This system is designed to regulate the pH and Total Dissolved Solids (TDS) levels in the nutrient solution. The monitoring component is facilitated by an Internet of Things (IoT) framework, which provides real-time data and remote oversight.

Key components of the control system include:

• pH and TDS Indicators: These are critical for assessing the nutrient quality and ensuring they remain within specified optimal ranges.
• Mamdani Fuzzy Inference System: This system determines the active periods of the pH Up, pH Down, and AB Mix nutrient pumps. Its fuzzy logic-based approach allows for nuanced decisions in response to varying pH and TDS levels.

The results from the experimentation are noteworthy:

1. pH Normalization: The control system can normalize the pH within one to three control steps. Each control step has a response time of approximately 60 seconds. Importantly, the system prevents oscillations in pH Up and pH Down adjustments, ensuring stability.
2. TDS Control: The system’s prediction of the active time needed for the AB Mix pump is accurate, enabling the TDS levels to normalize in a single control step.

The paper concludes that the designed control system is both efficient and effective. It successfully normalizes pH and TDS levels to meet desired standards, as validated by surface control simulations and real-world experimental data. Overall, this paper demonstrates a significant advancement in automated nutrient management in hydroponic systems leveraging fuzzy logic and IoT technologies.