HydroLab: A Module for the Investigation of Fertigation Strategies in Hydroponics

Abstract

In recent times, hydroponics has gained popularity as a viable solution to the challenges facing traditional agriculture, as it offers an environmentally friendly option with higher crop yields and improved quality, eliminating the need for soil. Despite these benefits, hydroponics still encounters significant difficulties, particularly in optimizing fertigation strategies. The literature contains several studies focusing on test benches for investigating technological recommendations related to fertigation strategies. However, there has been no proposed test bench capable of simultaneously exploring various types of fertigation strategies. In this work, HydroLab is introduced: a hydroponic module that allows for the simultaneous comparison of two types of fertigation strategies and enables remote monitoring and control through IoT functionality. Additionally, HydroLab’s potential to generate technological recommendations has been validated through a case study comparing time-based and solar radiation-based fertigation in lettuce cultivation. The case study successfully showcases the capability of HydroLab to investigate and compare different fertigation strategies. Given the global importance of food security challenges, this work is expected to contribute to the investigation of fertigation strategies in hydroponics.

1. Introduction

The agriculture industry currently faces a significant and unprecedented challenge: the efficient and timely production of food to meet the needs of a rapidly growing global population. According to the Food and Agriculture Organization (FAO) of the United Nations, the year 2050 is projected to be a pivotal year with profound global implications since the human population is anticipated to reach 9.73 billion individuals, with approximately 75% residing in urban areas [1]. Meeting the nutritional demands of this growing population will require significant increases in food production, estimated at approximately 70% compared to 2007 levels [2]. These challenges are compounded by a variety of factors, including resource constraints, environmental concerns, and supply chain inefficiencies.

The Green Revolution is one of the most significant events in agriculture history, marked by a series of research, development, and technology transfer initiatives aimed at bolstering industrialized agricultural production [3]. These initiatives focused on cultivating high-yield crop varieties, expanding fertigation infrastructure, and disseminating hybrid seeds that exhibited enhanced productivity. However, intensified soil utilization and increased reliance on fertigation were crucial factors that posed some of the most pressing challenges to agriculture during this time [3]. While the initiatives addressed the immediate needs, they had adverse repercussions, such as soil degradation, salinization of irrigated areas, overexploitation of groundwater resources, deforestation, greenhouse gas emissions, the exacerbation of the greenhouse effect, and contamination of water bodies with nitrates [4]. The long-term consequences of these actions have become increasingly apparent in previous years and different approaches must be investigated toward food security.

Hydroponics has gained popularity as it represents an environmentally friendly option that offers higher crop yields and improved quality [5]. Studies have indicated that the global hydroponic market is projected to experience significant growth, with an anticipated annual growth rate of 20.7% from 2021 to 2028, reaching a value of 9.8 billion [6]. This agricultural practice eliminates the need for soil and holds promise as a viable solution to the challenges facing traditional agriculture [2]. In hydroponics, plants are cultivated in natural or artificial substrates that facilitate the efficient uptake of nutrients from a carefully prepared nutrient solution. This method can address issues related to substandard crop quality and quantity, soil contamination, and the limited availability of arable land and water resources in traditional agriculture [7]. Despite these benefits, hydroponics still faces significant challenges, such as the optimization of fertigation strategies [8], particularly when considering different crops, varieties, and growing conditions [9,10].

Fertigation involves the injection of fertilizers into an irrigation system and it is the most critical activity in hydroponics. For instance, a high salt content in the nutrient solution generates osmotic pressure values that prevent the absorption of nutrients, while low content affects plant growth and productivity [11]. In some cases, frequent fertigation may be necessary due to the restricted root volume of the plants [12], but too frequent fertigation increases the moisture content in the root zone, reducing the oxygen availability to plants [13]. Moreover, fertigation requirements vary based on numerous factors such as plant species, cultivar, growth stage, utilized substrate, season, and climate conditions. Due to the lack of standard knowledge and the complexity of the problem, test benches are fundamental for investigating effective fertigation strategies.

Flexible test benches are valuable tools in agricultural research and development as they offer a controlled and adaptable environment for conducting experiments and evaluating the effectiveness of various fertigation approaches [14,15]. By utilizing test benches, researchers and practitioners can assess multiple fertigation strategies concurrently, considering factors such as water and nutrient efficiency, crop performance, and environmental impact. This facilitates the identification of the most suitable fertigation methods and allows for the refinement and optimization of integrated strategies.

Given the above, this work proposes a module for the investigation of fertigation strategies in hydroponics, defined as HydroLab. The article is structured as follows: Section 2 provides a comprehensive review of the current state-of-the-art in agricultural test benches. In Section 3, HydroLab is introduced, outlining its framework and implementation technologies. Section 4 presents a case study that demonstrates the potential of HydroLab in investigating different fertigation strategies. The obtained results are discussed in Section 5; finally, Section 6 presents the conclusions as well as the directions for future work.

2. State-of-the-Art

In the literature, several studies can be found that focus on test benches for investigating technological recommendations related to irrigation and fertigation strategies. In this section, these approaches are illustrated, outlining how they differ from the one proposed in this work.

Guerrero et al. [16] constructed an A-frame hydroponic structure to conduct their experiment, which included five distinct treatments replicated four times. The main focus of the study was to assess the impacts of different substrates on the physiological growth of lettuce plants. However, it is worth mentioning that the test bench was not located inside a greenhouse, which means that the microclimate surrounding the plants could not be controlled. As a consequence, the obtained results were highly influenced by the external environment, limiting the development of technological recommendations. Therefore, only studies conducted in greenhouses are considered.

Next, various works related to the proposal of test benches for investigating irrigation strategies are presented. Prado-Hernández et al. [17] conducted an experiment to assess the yield, width, and length of lettuce leaves under different irrigation configurations. Specifically, irrigation tape was installed at various depths: on the surface and at subsurface depths of 10, 15, and 20 cm. Lettuce plants were cultivated in plastic containers filled with sandy clay loam soil. The same time-based irrigation (i.e., periodic irrigation) was applied across all four depths, ensuring a controlled environment for the experiment. It is important to note that the proposed test bench allows for the investigation of irrigation tape installed at different depths. However, it does not facilitate the exploration of various irrigation strategies based on different scheduling events.

Nemali and van Iersel [18] propose an automated irrigation system capable of maintaining a desired level of volumetric water content (VWC) in the substrate of potted plants. Dielectric moisture sensors were employed to measure VWC every 20 min. When the measured VWC felt below the set point, the controller activated a solenoid valve to initiate irrigation. The effectiveness of the proposed irrigation system was evaluated through the cultivation of 16 independent groups of plants, each with a unique set point value. However, the authors did not utilize it as a test bench to provide technological recommendations.

Palumbo et al. [19] conducted a study to examine the effects of time-based irrigation compared to dielectric sensor-based irrigation. The experiment involved placing pots on 1% sloped PVC troughs, with each trough dedicated to a specific treatment. The cultivation of green beans was used to compare three treatments with different set points of VWC, along with a treatment involving time-based irrigation. It is important to note that in contrast to the approach proposed in our work, authors incorporated fertilizers directly into the substrate rather than injecting them into the irrigation system for fertigation purposes.

Although Nemali and van Iersel [18] and Palumbo et al. [19] proposed versatile test benches, their focus remained on the investigation of irrigation strategies. Consequently, we will next analyze works that specifically address fertigation. Martínez-Rodríguez et al. [20] carried out an experiment to compare the effects of two substrates on tomato cultivation: (i) tezontle and (ii) a mixture of tezontle with vermicompost. The irrigation volume and frequency were determined based on the water retention capacity of tezontle, and an automated system was used to deliver the nutrient solution to the plants. Each treatment, including a control group, consisted of 12 plants, resulting in a total of 36 plants for the experiment. It is important to note that while the experiment focused on comparing two substrates, the fertigation strategy remained consistent across all treatments.

Ta et al. [21] conducted a study to investigate the cultivation of paprika plants under different fertigation frequencies based on solar radiation. The plants were grown on rockwool slabs using the vertical trellis “V” technique. Fertigation was initiated when the cumulative solar radiation reached a specific set point. Two different set points were defined to assess their impacts on transpiration, growth, fruit yield, and water use efficiency. It is important to note that, in this study, the focus is on investigating different fertigation frequencies based on solar radiation, rather than comparing different types of scheduling events, such as time-based, substrate monitoring, or climate monitoring strategies.

A summary of the reviewed articles is provided in

Table 1. Classification of the works studied in the state-of-the-art analysis.

	Controlled Environment	Fertigation or Irrigation	Scheduling Event	Treatments
M. Guerrero et al. (2014) [16]	×	Fertigation	Time-based	Substrate
Prado-Hernández et al. (2018) [17]	✓	Irrigation	Time-based	Drip depth
Nemali & van Iersel (2006) [18]	✓	Irrigation	Substrate monitoring	Volumetric water content
Palumbo et al. (2021) [19]	✓	Irrigation	Time vs. substrate monit.	Irrigation strategy
Martínez-Rodríguez et al. (2017) [20]	✓	Fertigation	Time-based	Substrate
Ta et al. (2012) [21]	✓	Fertigation	Climate monitoring	Solar radiation

. It is evident that no test bench capable of exploring various types of fertigation strategies has been proposed thus far. Additionally, the analyzed studies do not employ Internet of Things (IoT) technologies, which could enhance system monitoring and control [22]. These two aspects form the novel contributions proposed in this work.

3. HydroLab

Li et al. [23] define the term ’framework’ as a conceptually layered structure of a system for a set of functionalities, and ’architecture’ as the instantiation of the framework through implementation technologies. In this section, HydroLab is introduced; it is differentiated into its framework and the corresponding architecture. This distinction allows for a comprehensive understanding of the HydroLab conceptual structure and the practical means by which it is implemented.

3.1. HydroLab Framework

HydroLab is a module designed to implement different fertigation strategies in two independent samples of plants. The module is illustrated in Figure 1.

The user manually prepares a nutrient solution with a predefined level of pH and electrical conductivity (EC). Prior to fertigation, an air compressor is activated to mix the solution, ensuring that concentrated salts do not deposit. Subsequently, the solenoid valve and pump are activated to initiate fertigation of the crop sample. The nutrient solution passes through a mesh filter to remove solid particles that could potentially damage the pump. Two sets of solenoid valves, pumps, and mesh filters are implemented to independently manage the two crop samples.

3.2. HydroLab Architecture

In this section, the framework previously illustrated is instantiated through implementation technologies. Furthermore, its sensors and control architecture are presented.

The objective of HydroLab is to develop fertigation strategies for hydroponics. In order to facilitate statistical analysis, the same strategy is applied to groups of crops. The module consists of two rows of plants, with each row containing eight plants (Figure 2). However, it may be scaled up to accommodate larger crop groups. On each row, two slabs with substrates are positioned on an inverted gutter structure equipped with lateral drain channels, as illustrated in Figure 3. The slope of the gutter is adjusted using suspensions to facilitate the drainage flow into a cistern located at the end of the structure. Each slab accommodates four plants, which are supplied with nutrients through spaghetti tubes connected to the main distribution line. Pressure compensating (PCJ CNL of 4 L/h) (https://www.netafim.com/en/products-and-solutions/product-offering/drip-irrigation-products/PCJ-online/ (accessed on 18 July 2023)) emitters are utilized to ensure precise and uniform irrigation for the plants [24]. These emitters include anti-draining features to prevent fluid leakage after pump shutdown, as well as self-compensating capabilities to maintain a consistent solution flow despite changes in line pressure. An SFDP1-011-070-21 (http://www.seaflo.com/en-us/product/detail/1070.html (accessed on 18 July 2023)) diaphragm pump is used to deliver the nutrient solution to the emitters in conjunction with a solenoid valve. This pump is chosen for its ability to handle chemical solutions within the acceptable pH range of 5 to 10. Drops are injected into the substrate using vertical stakes connected to the spaghetti tubes. Lastly, the EcoPlus HGC728459 (https://www.hawthornegc.com/shop/product/ecoplus-commercial-air-pumps (accessed on 18 July 2023)) compressor is employed to mix the solution before fertigation.

The sensors and control architecture of HydroLab are depicted in Figure 4. These components support the decision-making process concerning fertigation and are integrated with Internet of Things (IoT) technologies to facilitate remote monitoring and control of the system. Various sensors can be strategically placed based on specific experimental requirements. In particular, a pyranometer is utilized, as the case study described in Section 4 involves a comparison between a time-based strategy and solar radiation-based fertigation strategy. However, further sensors can be installed in the future (such as capacitance probes) to test fertirrigation based on the VWC [25,26]. Next, the different elements are illustrated.

• Pyranometer: A Hukseflux SR05-D1A3 (https://www.hukseflux.com/products/pyranometers-solar-radiation-sensors/pyranometers/sr05-d1a3-pyranometer (accessed on 18 July 2023)) pyranometer is utilized to measures solar radiation and temperature. The acquired values are transmitted to the Raspberry Pi using the Modbus protocol;
• Programmable logic controller (PLC): The Beckhoff CX7000 (https://www.beckhoff.com/en-en/products/ipc/embedded-pcs/cx7000-arm-cortex-m7/cx7000.html (accessed on 18 July 2023)) PLC controls the HydroLab sensors and actuators. It transmits the acquired sensor data to the gateway through the OPC UA protocol;
• Router: The ADSL2+ HG5316 (https://etb.com/mejorainternet/info/docs/HG531V1.pdf (accessed on 18 July 2023)) router facilitates the establishment of a closed communication network between the gateway and the PLC. It also generates a wireless network with internet access, enabling remote control of the system;
• Gateway: As the utilized PLC does not have wireless communication capabilities, a Raspberry Pi is used as the gateway. The Raspberry Pi utilizes the Node-RED (https://nodered.org/ (accessed on 18 July 2023)) programming tool to facilitate communication with the PLC and the HMI (Human-Machine Interface). Additionally, the Raspberry Pi processes the solar radiation and temperature data and initiates fertigation when a specified threshold is reached. The fertigation command is sent to the PLC, which is responsible for its implementation;
• Camera: The camera model Tapo C200 (https://www.tp-link.com/co/home-networking/cloud-camera/tapo-c200/ (accessed on 18 July 2023)) enables remote audiovisual monitoring of the crops. The camera can be operated through its dedicated smartphone application. It can also be adapted for viewing on any computer using emulators. Researchers and practitioners can monitor the development of their crops without the need for physical presence on-site;
• HMI: The system’s control panel is implemented using the Node-RED interface, enabling the reading and writing of the system’s state from a personal computer.

Finally, a breakdown decomposition of the cost for implementing the module is represented in

Table 2. Cost breakdown analysis of HydroLab.

Component	Units	Cost [USD]
Gutter structure	2	636
Solenoid valve	2	14
Compressor	1	241
Router	1	33
Video camera	1	48
Mesh Filter	2	34
Diaphragm pump	2	80
Raspberry Pi	1	34
Pyranometer	1	950
PLC	1	493
Total cost		2563

.

4. Case Study

This section presents the experiment used to demonstrate the potential of HydroLab in investigating different fertigation strategies. The experiment took place from 31 March to 28 April 2023, and aimed to compare two treatments: a time-based and a solar radiation-based fertigation strategy in the cultivation of lettuce. The experimental setup is described in Section 4.1, while the compared fertigation strategies are detailed in Section 4.2.

4.1. Experimental Setup

HydroLab was utilized as the test bench for the experiment as it allows the cultivation of two independent crop samples. To ensure a fair and controlled comparison, all variables were kept consistent between the two crop samples, except for the triggering event that initiated the fertigation process. The adjustable suspension was set to an inclination of 5° for both samples, and lettuce seedlings grown under the same conditions were used.

Coconut coir was adopted as the substrate since [27]: (i) it exhibits excellent water retention capacity, allowing for proper moisture levels within the root zone, which is essential for the growth and development of lettuce plants; (ii) it possesses adequate drainage properties, ensuring the efficient removal of excess water and preventing waterlogging, which can be detrimental to plant health; (iii) the physical structure of its fibers provides a favorable environment for root penetration and aeration, facilitating nutrient uptake and promoting robust plant growth. Coco Elite (https://saenzfety.com/wp-content/uploads/producto/176/pdf/ft_coco_elite.pdf (accessed on 18 July 2023)) was used due to its fine particle size, excellent moisture retention, and good aeration properties.

The module was installed within the Uniandes Controlled Environment Agriculture Laboratory (https://revistacontacto.uniandes.edu.co/contacto-21-transformacion-digital/un-banco-de-pruebas-para-optimizar-cultivos-hidroponicos/ (accessed on 18 July 2023)) (Figure 5), which is an 18 m${}^2$ glass greenhouse located at 4${}^{°}$ N, 74${}^{°}$ W. This setting ensured that both crop samples experienced the same microclimate conditions, reducing environmental variability during the experiment.

The utilized nutrient solution is illustrated in

Table 3. Nutrient solution utilized for the experiment.

Nutrients	Quantity [g/10 L]
Calcium nitrate	6.10
DTPA Fe	0.19
Potassium nitrate	4.20
Magnesium sulfate	3.60
Monopotassium phosphate	2.10
Manganese sulfate	0.19
Zinc sulfate	0.07
Copper sulfate	0.06
Solubor Fe	0.11

. This recipe was provided by Sáenz Fety (https://saenzfety.com/ (accessed on 18 July 2023)). During the preparation of the solution, meticulous attention was given to pH and EC measurements. As suggested in [28], the pH of the solution was carefully adjusted and maintained within the range of 5.8–6.0, while the EC was between 2.2–2.4 dS/m.

4.2. Compared Fertigation Strategies

In this section, the compared fertigation strategies are detailed.

Treatment 1: Time-based strategy. To prevent water stress and ensure proper hydration of the crop, a daily volume of 350 cc of nutrient solution was applied [17]. Four irrigation events were scheduled throughout the day, with each event delivering 1/4th of the daily volume (i.e., 87.5 cc), i.e., (i) at 7:00 a.m., to hydrate the plants before the temperature rise; (ii) at 10:00 a.m., to provide a refreshing effect on the crop; (iii) at 2:00 p.m., to minimize excessive evaporation losses; (iv) at 5:00 p.m., to support crop recovery before the end of the day.

Treatment 2: Radiation-based strategy. Evapotranspiration, which encompasses both plant transpiration and surface evaporation, is a fundamental process in plant physiology that involves the loss of water from plants. Accurate estimation of water losses is essential for effective fertigation management. In our study, we utilized the Hargreaves and Samani equation as the fundamental framework for estimating evapotranspiration rates and, consequently, the amount of nutrient solution provided during each fertigation [29]. This equation allows for the estimation of water lost by the crop during a given time period. Next, the Hargreaves and Samani equation is presented:

$$E{T}_0=0.0135(T_{avg}+17.38)R_s$$

(1)

where $E{T}_0$ (cc) is the estimated evapotranspiration (per plant) in the considered time period, $T_{avg}$ (°C) is the mean temperature, and $R_s$ refers to the incident solar radiation converted to cc/°C.

When converting the incident radiation to evapotranspiration in cc, it is important to consider the complex processes occurring within crops. Simply estimating it based on the energy values required for water evaporation is insufficient. To address this, the FAO guidelines were followed [30]. The guidelines offer a comprehensive framework for adjusting incident solar radiation values and obtaining accurate estimations of water loss through evapotranspiration. As recommended by the FAO, the following conversion factor was applied to $R_s$:

$$2.45\left[{\mathrm{J}/\mathrm{cm}}^2\right]=1\left[{\mathrm{cc}/}^{°}\mathrm{C}\right]$$

(2)

Next, we show how this mathematical model was utilized to calculate the nutrient solution amount provided during each fertigation. Following the recommendations of Sáenz Fety, a solar radiation threshold of 160 J/cm${}^2$ was set. This means that when the accumulated solar radiation overcame this value, fertigation was triggered. By applying Equation (2), $R_s$ was computed in 65.3 cc/${}^{°}$C. Then, $E{T}_0$ was estimated through Equation (1), considering the average temperature between the previous and the current fertigation.

It is recommended that the amount of nutrient solution supplied during fertigation matches or slightly exceeds the amount of water lost through evapotranspiration ($E{T}_0$). This approach ensures that plants have adequate water supply for their physiological processes, including nutrient absorption, photosynthesis, and growth. By preventing water deficiency, healthy plant development can be promoted [31]. In our experiment, the calculated $E{T}_0$ value was used as the nutrient solution volume delivered during the fertigation process, ensuring that the plants received the necessary water and nutrients for optimal growth.

5. Results and Discussion

Next, the results of the case study presented in Section 4 are illustrated. The indicators defined for comparing the two fertigation strategies are introduced in Section 5.1, and their evaluations for both the time-based and radiation-based fertigation are described in Section 5.2. Finally, Section 5.3 discusses the obtained results and the potential of using HydroLab as a module for investigating fertigation strategies in hydroponics.

5.1. Selected Indicators

Considering that the objective of the case study was to demonstrate the potential of HydroLab in investigating different fertigation strategies, emphasis was placed on computing economic and morphological indicators and quantifying the utilized resources, i.e., nutrient solution consumption. Whereas, the calculation of physiological indicators will be left to future experiments in which HydroLab will be utilized as a tool to provide technological recommendations.

In line with the concept of the performance measurement system suggested in maintenance management [32], we opted to compute both leading and lagging indicators. As defined in [33], a leading indicator is a quantifiable factor that undergoes changes before the studied phenomenon starts to exhibit a specific pattern or trend. On the other hand, a lagging indicator is a measurement that only shifts after the phenomenon has already changed. In the context of agriculture, leading indicators can be viewed as metrics that describe the growth process, while lagging indicators assess the outcomes of the cultivation.

The indicators selected for quantifying the results of the case study are as follows:

• Height: average height of the plants in each crop row;
• Weight: average weight of the plants in each crop row;
• Nutrient solution consumption: the amount of nutrient solution utilized for each crop row;
• Yield: the ratio between the final weight of the row of lettuce and the occupied space;
• Nutrient solution efficiency: the ratio between the final weight of the row of lettuce and the consumed nutrient solution;
• Cumulative height: the curve that describes the changes in the crop height over time. This plot is generally defined as the sigmoid curve [34];
• Cumulative nutrient solution: the curve that describes the cumulatively delivered nutrient solution over time.

The selected indicators are presented in

Table 4. Selected indicators for the comparison of time-based vs. radiation-based fertigation.

Indicator	Category	Leading or Lagging	Dimension
Height	Morphological	Lagging	[Length]
Weight	Morphological	Lagging	[Mass]
Nutrient solution consumption	Resource	Lagging	[Volume]
Yield	Economic	Lagging	[Mass]/[Area]
Nutrient solution efficiency	Economic	Lagging	[Mass]/[Volume]
Cumulative height	Morphological	Leading	[Length]
Cumulative nutrient solution	Resource	Leading	[Volume]

. It can be observed that lagging indicators consist of single values as they indicate results, while leading indicators are represented through curves as they assess the growth process over time. Finally, yield and nutrient solution efficiencies are considered higher-order indicators. Yield is determined through the weight of the harvested crops, while nutrient solution efficiency takes into account both the crop weight and the amount of the consumed nutrient solution. These indicators were chosen because the yield reflects the effectiveness of the implemented fertigation strategy, while the nutrient solution efficiency represents its efficiency in terms of resource utilization. In the context of the experiment, effectiveness refers to the extent to which the fertigation strategy achieves the desired outcome (i.e., lettuce weight), while efficiency pertains to the optimal use of resources (i.e., nutrient solution) to accomplish the goal.

5.2. Results

Through the utilization of HydroLab, the experiment described in Section 4 was implemented. Weekly replenishment of the nutrient solution was carried out by filling tank T320 (Figure 1). A storage tank with a capacity of 60 L was utilized and regular cleaning of the reservoir was performed weekly to prevent calcification. The lettuce height measurements were taken every 2–3 days. The data regarding the amount of consumed solution were recorded and stored in the PLC. Figure 6 illustrates the two rows of plants on day 19, providing a visual representation of the system. Harvesting of the lettuce crops took place on day 30.

The experiment was initiated shortly before the Easter holidays (when the university was closed). Despite this, the system operated autonomously, and the authors were able to remotely monitor its proper functioning through the HMI interface that was implemented.

Next, the leading indicators defined in Section 5.1 are presented to illustrate the growth process. Figure 7 shows the sigmoid curve of the two rows of plants. The curve represents the average height of each plant sample, and the error bars encompass both measurement uncertainties associated with the instrument and statistical variations. It can be observed that:

• The crops started at approximately the same height, as lettuce seedlings grown under identical conditions were used;
• The first assessment was conducted on day 0, while the second took place on day 10. This was because the university was closed during the Eastern holiday and the height was manually assessed. In future research, the possibility of utilizing a camera for automatic height assessment could be explored;
• The radiation-based fertigation treatment exhibited greater growth compared to the time-based fertigation one.

Figure 8 illustrates the cumulative nutrient solution indicator, representing the total solution consumed by each plant sample over the course of the 30-day experiment. The curve representing the treatment under time-based fertigation shows a consistent slope, indicating regular scheduling and a constant volume of the delivered nutrient solution. Conversely, the curve for the treatment under radiation-based fertigation exhibits variable slopes, reflecting fluctuations caused by the changing radiation and temperature conditions in the city of Bogotá. It is worth noting that while height measurements were manually assessed, the consumed nutrient solution was automatically quantified and recorded in the PLC. Therefore, data are available even during the Eastern holidays period.

Finally, the computed lagging indicators are presented in

Table 5. Lagging indicators obtained in the implemented experiment. Height, weight, and nutrient solution consumption were calculated based on the average of the 8 plants in each row.

Strategy	Height [cm]	Weight [g]	Nutrient Solution Consumption [L]	Yield [g/cm${}^2$]	Nutrient Solution Efficiency [g/L]
Time-based Fertigation	23.9 ± 0.8	218.0 ± 7.4	10.9	2.2	20.0
Radiation-based Fertigation	27.2 ± 0.6	256.2 ± 8.0	9.8	2.6	26.1

. It is evident that the treatment under radiation-based fertigation exhibited better height and weight compared to the one under time-based fertigation, while also consuming a lower amount of nutrient solution. This superior effectiveness and efficiency of the treatment is reflected in the two higher-order indicators: yield and nutrient solution efficiency.

5.3. Discussion

In this section, the obtained results are discussed, focusing on the comparison between time-based and radiation-based fertigation strategies, as well as the potential of HydroLab as a module for investigating fertigation strategies in hydroponics.

The implemented experiment demonstrated that radiation-based fertigation appears to be a more effective and efficient strategy compared to time-based fertigation. This is supported by higher values of the yield and nutrient solution efficiency indicators. While these results are promising, further experiments are necessary before turning this treatment into a technological recommendation. Additionally, it is important to monitor more variables, such as the microclimate within the greenhouse, and to compute further indicators, including physiological ones. Physiological indicators can provide insights into the quality of the produced crop. This is crucial because effectiveness is determined by yield as well as the quality of the harvested crop.

Concerning HydroLab, the module facilitated the examination of two categories of fertigation strategies: time-based and climate monitoring. Additionally, the implementation of IoT functionality enabled remote monitoring and control of the module. Therefore, the concept holds promise for investigating various fertigation strategies in hydroponics, although enhancements are needed to generate technological recommendations. These enhancements involve (i) expanding the crop rows to allow for the simultaneous investigation of multiple treatments; (ii) increasing the sample size per treatment by accommodating a greater number of plants per row. However, compared to the state-of-the-art analysis presented in Section 2, the proposed test bench stands out due to its ability to investigate different fertigation strategies and incorporate IoT technologies for improved system monitoring and control.

6. Conclusions and Future Work

Food security poses a critical challenge as the global population surpasses 7.8 billion and is steadily growing. Traditional soil-based agriculture has the capacity to produce significant quantities of food, but it often comes with negative environmental impacts. In response to this context, hydroponics has emerged as a popular solution. Hydroponics offers an environmentally friendly alternative that promotes higher crop yields and improved quality, while eliminating the reliance on soil.

In hydroponics, fertigation is recognized as a pivotal activity, and its demands are subject to numerous factors, including plant species, cultivar, growth stage, utilized substrate, season, and climate conditions. Given the absence of standardized knowledge and the intricacy of the matter, test benches play a fundamental role in exploring and evaluating effective fertigation strategies.

The literature presents various test benches designed for investigating fertigation strategies. However, to the best of the authors’ knowledge, no test bench has been proposed that is capable of simultaneously exploring multiple types of fertigation strategies.

Based on the aforementioned considerations, the objective of this research was to propose a flexible test bench capable of offering a controlled and adaptable environment for conducting experiments and evaluating the effectiveness and efficiency of various fertigation strategies. To fulfill this objective, the researchers developed HydroLab, a module that enables the simultaneous comparison of two types of fertigation strategies and can be remotely monitored and controlled through IoT functionality.

To validate the effectiveness of HydroLab in investigating fertigation strategies in hydroponics, a case study was conducted that compared a time-based and a solar radiation-based fertigation strategy in lettuce cultivation. Throughout the experiment, all variables were carefully controlled and maintained consistent between two independent crop samples, except for the triggering event that initiated the fertigation process. The HydroLab module operated autonomously during the case study, and the authors were able to remotely monitor its proper functioning through the implemented HMI interface. The results of the case study demonstrated that solar radiation-based fertigation outperformed the time-based strategy in terms of effectiveness and efficiency, as indicated by higher yield and nutrient solution efficiency. While these findings are promising, further experiments are necessary before considering radiation-based fertigation as a technological recommendation. Nevertheless, the case study successfully showcases the capability of HydroLab in investigating and comparing different fertigation strategies.

Notably, the proposed test bench constitutes a preliminary concept that should be further validated and improved in the future. Some future works identified are:

• Nutrient solution module: In HydroLab, the crop rows receive the same nutrient solution. Therefore, it would be desirable to have a module able to automatically generate a nutrient solution with pre-set values of pH and EC for each crop row;
• Technological recommendations: Enhancements are needed to convert HydroLab into a tool capable of providing technological recommendations. These enhancements involve (i) expanding the crop rows to allow for the simultaneous investigation of multiple treatments; (ii) increasing the sample size per treatment by accommodating a greater number of plants per row;
• Crop measurement system: In line with the concept of the performance measurement system, a framework of indicators targeting the generation of technological recommendations should be defined. This framework should include both leading and lagging indicators, encompassing various categories, such as morphological and physiological indicators. Additionally, trends should be identified to leverage leading indicators as a tool for diagnosing the positive and negative aspects of fertigation strategies.