The integrated nutrient solution mixer nodes can be remotely controlled by the upper smart greenhouse environmental control system. They manage all data acquired from the lower nutrient solution mixer nodes, enabling AI analysis utilizing big data. This data can then be visualized and utilized in smart farm systems.
Creating the most suitable business for the certified nutrient solution facility in a SMART FARM setup, as depicted in Fig 4. involves storing data generated in the field on a web server. This data, along with additional inputs such as production quantity and quality data, undergoes analysis and processing. By establishing benchmarks based on the analyzed data, performance evaluation criteria for the nutrient solution system are provided. Operating on a foundation of reliable data, it is anticipated that this approach will not only facilitate nutrient solution facility certification but also lead to improved quality.
As depicted in Fig 4, data obtained from sensors includes basic values such as temperature, pH, CO
2, light intensity and humidity. These values are fundamental for plant growth, as they encompass essential elements such as nitrogen, sulfur, magnesium, calcium, boron and others, which significantly influence plant growth. By analyzing the nutrient content of major elements that have a significant impact on the growth of each organism, productivity can be enhanced using tracked data.
Furthermore, considering that the wavelength of light affects plant growth, by combining appropriate data from the plant’s light absorption spectrum and pattern with data from the nutrient solution mixer, effective data for growth can be extracted (Table 2). Utilizing this data, adjustments can be made to the wavelength and sunlight exposure based on the growth stage of the crops, establishing optimal conditions for factors such as sweetness, texture, and taste, thereby enhancing the quality of the crops.
To ensure scalability in SMART FARM systems, services are configured to provide functionalities similar to those of touch screens in indoor smart cultivation machines. These services are designed to be processed through Open APIs, creating user-friendly programs for universal use. Remote control features are designed to monitor and control crop growth status and various environmental information in indoor smart cultivation machines. Additionally, in the event of abnormalities in indoor smart cultivation machines, it is essential to have the capability for users to receive information externally. Therefore, functionalities allowing users to set alert transmission conditions for sensors and devices are necessary.
Utilizing an integrated control configuration, a register map and middleware server are set up to enable consistent operation regardless of the type of nutrient solution mixer. This ensures that the same conditions can be applied even with different types of nutrient solution mixers, allowing for uniform operation according to user instructions. Furthermore, this system is accessible for monitoring through a SMART FARM dedicated dashboard (Fig 5 and 6). It enables bi-directional communication for monitoring and control, allowing sensing of nutrient solution, light wavelengths, and additional nutrients. Users and testers can directly observe and verify this information.
Further-more, by pursuing diversity in sensor nodes, various sensing capabilities are accommodated. The design includes expansion ports numbered from 1 to 10, allowing for the addition of various sensors in the future with scalable functionality. This configuration enables not only nutrient solution mixers but also optical generators and control of actuators such as valves to be incorporated, enhancing versatility by facilitating the addition of various types of data
(Choi et al., 2020).
Nutrient Solution System Performance Evaluation Methods
The types of nutrient solutions vary widely, and they should be selectively used depending on the type of crops and their growth stage. In this study, a method is employed where concentrated fertilizers added to supply a required amount of ions to the drainage are diluted with water and distributed to crops. Additionally, the concentration ratio of the fertilizer is adjusted and maintained under uniform light conditions in the SMART FARM implementation site to measure the growth status.
The nutrient mixing system is composed of a control unit and a supply unit. To control the entire system, a PC-based dashboard control method is employed and the control program is accessible through various applications
via direct communication with the dashboard. To accurately supply the required amount of concentrated nutrient solution determined by algorithms, the supply unit utilizes valves with flow control capability and metering pumps equipped with motors. Additionally, a dilution motor is installed to eliminate pulsations generated during the repeated discharge and suction of the pump and to dilute the nutrient solution. Dilution and supply are carried out simultaneously after a 10-minute dilution period, thereby increasing productivity.
To evaluate the performance of the system, the accuracy and effectiveness of nutrient adjustment in the recirculating nutrient solution system are examined. This involves investigating the pH and electrical conductivity (EC) of the nutrient solution during the lettuce growth period. During actual nutrient adjustment, the amounts of each major element required, based on the algorithm, are checked against the amounts of nutrients automatically dispensed by the nutrient solution system. Finally, the ion content of the nutrient solution dispensed during irrigation and the target values for each element are compared and analyzed for evaluation.
Additionally, for each supplementation, the supplied nutrient solution is prepared in batches of 50 liters and calculated accordingly. Drainage is measured precisely using a scale to dilute the nutrient solution according to a predetermined ratio.
To evaluate the performance, the growth status of each crop under different nutrient compositions is measured quantitatively using overall size and weight (Table 3). Additionally, measurements of moisture content and sugar content are taken. These data are then combined with cultivation environment data to quantitatively measure growth under each condition, necessitating performance evaluation.
Moreover, all crops in the entire chamber must utilize the same species and seed conditions. Additionally, artificial light wavelengths and frequencies must be consistent across the experiments.
Based on such performance evaluations, data on nutrient system configurations and composition should be accumulated on a dashboard for a period ranging from 3 to 6 months with the same crop. Additionally, a server for storing accumulated data must be configured. Since there may be interruptions in power or changes in temperature conditions due to weather fluctuations during the test period, thorough supervision and management are necessary. Implementing such a performance evaluation system is expected to accurately identify the growth factors of nutrients and crops, thereby increasing productivity (
Jong-Dae and Dong-su, 2014).
The biggest advantage of utilizing such data-driven tests is the ability to cultivate and produce crops directly in SMART FARM fields while simultaneously conducting testing and sales. It is expected that the improvement in crop quality will increase annually, and by collecting performance evaluation indicators of SMART FARM data among agricultural associations and conducting big data analysis, the reliability of the nutrient solution system evaluation is expected to further increase (
Jong-Dae and Dong-su, 2014a).
