Fertilizer Mixing Device for Fertigation System Integrated with Home Assistant Platform ()
1. Introduction
The fertigation system is an advanced water and fertilizer management approach that delivers irrigation and nutrients simultaneously to plants. This system applies a dilute fertilizer solution multiple times per day, based on the specific needs of each plant, ensuring precision and adequacy. Fertigation has become an attractive method of fertilization in modern intensive agriculture systems. This has assumed added importance after the introduction of micro-irrigation systems like drip irrigation in irrigated agriculture [1]. The system optimizes the nutrient balance in soils by supplying the nutrients directly to the effective root zones as per the requirement, reducing labor and energy costs [2]. Additionally, fertigation minimizes environmental impact by reducing nutrient runoff and improving nutrient uptake efficiency [3].
Fertigation promotes plant health by reducing the risk of soil-borne diseases such as Fusarium, Rhizoctonia, and bacterial wilt, which are typically transmitted through the soil. This approach supports sustainable farming practices, leading to improved crop yield, healthier plants, and more efficient resource use. Fertigation offers several advantages, including enhanced water and fertilizer use efficiency, reduced nitrogen losses due to leaching, direct nutrient delivery to the root zone in readily available forms, better control over nutrient concentrations in the soil solution, and cost savings in application [4]. Moreover, integrating IoT-based monitoring systems in fertigation has proven to further enhance resource management and crop productivity. For a fertilizer mixing system, the main requirement is to determine the EC value required for a particular crop. Each crop requires a different amount of EC value based on the type and stage of growth. Usually, the quantity of fertilizer is given according to the age of the plant, which is crucial for plant growth because if the EC value is lower than recommended, the plant may not receive enough nutrients. The goal of this project is to produce an automatic fertilizer mixing system that is used with a fertigation system.
2. Fertilizer Mixture System
The fertilizer mixture for the fertigation system consists of component A, which includes calcium nitrate and iron, and component B, which consists of potassium nitrate, magnesium sulfate, monopotassium phosphate, manganese sulfate, zinc sulfate, copper sulfate, boric acid, and ammonium molybdate. Traditionally, the mixing of fertilizers for fertigation systems is done manually, where Components A and B are combined with water in a single tank. However, for large-scale fertigation systems serving hundreds of plants, the need for a larger fertilizer tank makes manual mixing increasingly inefficient and challenging. The most common methods for mixing fertilizers and water during irrigation include mixing tanks, injectors for applying fertilizers, and devices mounted on the hydrant of an irrigated area [5]. Emerging algorithms and technologies in water and fertilizer integration aim to address challenges such as improving blending response speed, stability, and enabling remote blending control. Experimental results have shown that the control strategy significantly enhances performance [6]. Numerous innovations in mixer method of AB fertilizer have been designed. Studies on multi-channel fertigation machines have proven successful in improving water and nutrient efficiency [7]. Additionally, integrating a microcontroller with a Human Machine Interface (HMI) allows farmers to easily monitor and operate the fertilizer mixing system via a smartphone [8]. Overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems [9]. The design of an IoT-enabled fertilizer mixing system includes several interconnected components for optimal performance and efficiency. These components include the fertilizer unit, pump, electronic valves, fertilizer mixing container, sensor nodes, and a cloud database for real-time data storage and analysis [10]. This project integrates a Wi-Fi-enabled device with the Home Assistant platform to enhance functionality and automation.
3. Methodology
The development of the system is to facilitate the work of mixing fertilizers at the Urban Farm Fertigation Project of Sultan Idris Shah Polytechnic. This project has received funding from Maybank to assist and provide modern agricultural knowledge to the zakat Asnaf among students. The original system used timer control where fertilizer was given to the trees 3 times a day with timer control. The fertilizer mixing system A and B were done manually. To facilitate the mixing of fertigation fertilizers, a system for mixing fertilizers A and B was developed based on IOT as shown in Figure 1.
Figure 1. Fertilizer system.
This fertigation system is designed to irrigate 300 tree bags, utilizing two 400-gallon polyethylene tanks. The AB fertilizer is separately diluted in two water barrels (fertilizer A and B). This system is specifically designed for the cultivation of Chili trees, as shown in Figure 2 meanwhile Figure 3 illustrated a complete system of fertigation.
The improvement to the existing mixing system is to use a multi-input sensor that can measure electrical conductivity (EC), temperature, Total Dissolve solid (TDS), potential of hydrogen (PH), salinity, oxidation-reduction potential (ORP) and conductivity factor (CF). The system can be controlled via mobile phone and via Home Assistant platform as shown in the system (Figure 3) and also the system flowchart (Figure 4). Table 1 shows the devices involved in developing this system which consists of wifi sensor EC, 2 dosing pump, wifi smart socket and wifi control valve.
Figure 2. Urban farm fertigation site.
Figure 3. Illustrated system.
Table 1. Equipment and device of the system.
Equipment/Device |
Specifications |
Quantity |
Yieryi-Tuya Smart WiFi
water quality |
pH 0.00 - 14.00 pH EC: 0 - 19,000 us/cm TDS: 0 - 19,990 ppm Temp: 0˚C - 50˚C |
1 |
Wifi Socket |
Wifi standard: Wifi 2.4 Ghz Wifi Frequency: IEEE 802.11 |
3 |
Wifi Control Valve |
frequency: 2.4 G Gate valve pressure: 1.6 Mpa |
4 |
Dosing Pump |
Voltage: 12 V Wattage: 5 W Flow rate: 100 ml/min Noise: less than 40 DB |
2 |
Figure 4. Flowchart of the system.
Table 1 shows the devices involved in developing this system which consists of Smart WiFi water quality, dosing pump and wifi control valve.
4. Result and analysis
The EC value in this system can be set depending on the needs of the plants planted in the fertigation system. There are 4 type of EC value setting which is 1.8 µs/cm, 2.4 µs/cm, 2.8 µs/cm and 3.0 µs/cm. Control and monitoring can be done either using a phone or through a home assistant. The developed system allows for control and monitoring via both mobile phone and computer, with integration into the Home Assistant platform, as illustrated in Figure 5. Testing of the system was carried out by looking at the EC accuracy value that had been set. In this experiment, the value of the fertilizer mixture A and B was set at 2.1 mS/cm. The EC reading from the sensor was compared with the meter reading. Figure 6 shows water parameters which are TDS, salt level, Ph, water temperature and EC value readings from the Water quality sensor while Figure 7 shows a comparison of the EC readings from the water quality sensor and the EC meter.
Figure 5. Dashboard of the system.
Figure 6. Water Quality parameter recorded.
A total of 50 samples were obtained. There was no significant change between the readings from the water quality sensor and the EC meter. The average EC reading for the water quality sensor was 2.09 ms/cm while the reading from the EC meter was 2.12 ms/cm. The difference value for the devices was 0.03 ms/cm.
Figure 7. Comparison EC value between water quality sensor and EC meter.
In addition, the system was also tested for effectiveness in terms of control and monitoring in several places around Sabak Bernam. Table 2 shows the results of the system tests in several locations. As a result of the tests, the overall system can be accessed and controlled according to the testing location. The results of the tests conducted show that the system can be reached and all devices can be controlled in the selected locations.
Table 2. System testing at several locations.
Location |
Distance from Device |
Condition |
Parit Baru |
3.9 Km |
success |
Kampung Sungai Apong |
4.9 Km |
success |
Bagan Nakhoda Omar |
11.1 Km |
success |
Pekan Sabak Bernam |
24.4 Km |
success |
Pekan Sungai Besar |
28.5 Km |
success |
Sungai Hj Dorani |
34.7 Km |
success |
5. Conclusion
The research proposes the development of fertilizer mixing systems integrated with home assistant platforms. The system can be accessed through Home Assistant mobile application and using computer. The system development allowed users to control and monitor EC value of the fertilizer in the tank. The system helps to facilitate fertilizer mixing which was previously done manually. The use of this system can save time, reduce farmers’ workload and reduce manpower in managing fertigation fields. The results of the test show that there is no significant difference between the readings from the water quality sensor and EC meter. The system can also provide a good response during testing in several specific locations. Although the system successfully controls all devices installed in the system, the use of wifi valve to control the flow of fertilizer sometimes causes misalignment. Therefore, it is recommended that other types of devices can be used to replace the use of wifi valve. In addition, in the future, it is also proposed that EC value measurements are also measured on each tree.
Acknowledgements
Thanks to Urban Farming Maybank Project to provide financial support and Department of Electrical Engineering, Polytechnic Sultan Idris Shah for providing facilities and instruments during the experiment. We would also like to thank the Smartgreen team for helping us prepare the device in this study and not forgetting all those involved directly and indirectly in completing this study.
Conflicts of Interest
The authors declare no conflicts of interest.