Conceptual Design Based on Modular Platforms for a Prototype of a Functional Growth Chamber for Cuttings in Controlled Agriculture

Abstract

Agricultural research and propagation systems often suffer due to a lack of access to affordable, adaptable, and well-structured technological solutions. Traditional plant growth devices typically rely on ad hoc construction, which limits their scalability, reuse, and adaptability. This study employs a user-centered conceptual design methodology based on product platform development and modular architecture to design a growth chamber for plant cuttings. The approach followed three main phases: (i) identification and classification of user needs, (ii) functional modeling of the base system and its variants, and (iii) architectural modularization through heuristic principles. Interviews with researchers yielded 55 functional requirements, of which 26 were defined as essential. Functional models were developed for both a base system and two variant systems incorporating alternative irrigation and sensing technologies. Heuristic analysis identified independent modules, such as irrigation, lighting, environmental monitoring, and control. Subsequently, block diagrams were used to translate functional logic into spatially coherent conceptual designs. The resulting architecture supports modular integration, reconfiguration, and scalability for diverse experimental needs. This work demonstrates that structured design methodologies, which are commonly used in industrial contexts, can be effectively applied in agricultural research settings to produce solutions that are versatile, low-cost, and have enduring value, offering a pathway for innovation, reproducibility, and technology transfer in resource-limited environments.

1. Introduction

Conventional or geoponic agriculture has historically been the mainstay of global food production. However, its environmental impact is significant due to the intensive use of resources such as soil and water, as well as its use of unsustainable agronomic management practices [1,2]. More than 70% of the freshwater available on the planet is used for agriculture, while approximately 15% of arable land has lost its productive capacity due to inadequate management practices [3,4]. This situation is exacerbated by the expanding need to supply a growing population under increasingly unstable climatic conditions.

In response, alternative technologies have emerged that optimize resource use, such as hydroponics, aeroponics, and controlled environment agriculture, a term that encompasses, among others operations, controlled growth chambers [5]. These allow agricultural experimentation to be conducted in small spaces and under controlled conditions [2,6]. Growth chambers, in particular, are used in research on plant physiology, crop propagation, and the development of new agronomic techniques. However, most of these systems are expensive, inflexible, or designed for specific commercial applications, which limits their adoption in academic, institutional, or research settings with restricted budgets [7,8,9].

Recent studies have demonstrated a growing interest in modular and automated systems applied to controlled-environment agriculture. For instance, Bua et al. (2023) developed a modular smart hydroponic greenhouse integrating IoT and edge-computing architectures for precise climate regulation [10]. Similarly, Patel et al. (2024) presented a hydroponic tower system designed with structural optimization to support modularity and reconfiguration; however, it is not directly intended for controlled environments [11]. Additionally, Fue et al. (2020) reviewed modular robotic platforms for field operations, highlighting their scalability and adaptability to diverse agricultural contexts [12]. These works confirm the relevance of modularity in agricultural innovation and underscore the opportunity to apply structured product platform methodologies to prototype development for experimental cultivation systems.

The development of automated or customized systems was noted within the reviewed literature; however, they often lack a methodological structure that enables their reuse or adaptation to multiple experimental contexts. Examples include, studies such as those of Nikolov et al. (2023) [13] and Wright et al. (2023) [14], who developed low-cost culture chambers but did not implement modular design strategies or product architecture. This methodological absence limits the experimental adaptability within these same prototypes and reduces scalability, hindering technology transfer to other projects or scientific communities.

In the face of these challenges, applying structured design methodologies in the early stages of prototype development represents a promising strategy. Design-based methodologies for developing product platforms and modular architecture have proven effective in various sectors, including manufacturing, household appliances, transportation, and consumer products [15,16]. Recent studies have documented their application in the design of coffee makers, bicycles, and electronic systems [17,18]. This type of design methodology prevents future problems by providing sufficient information and employing an analysis process in product development, requiring a lower expenditure of resources [19]. However, its adoption in the agricultural industry remains limited. Suñer (2017) observed that the only way to integrate modular design within the agricultural sector is through machinery [20], which would be impossible without the development of prototypes, namely, agricultural experimental devices. These devices are even scarcer, especially when it comes to controlled cultivation. This gap represents an opportunity to expand the scope of these methodologies to include applications with a high potential for social and scientific impact.

Modular design provides critical advantages that include scalability, which enables the incorporation of new functions; ease of maintenance; and the ability to adapt to different contexts or user requirements without redesigning the entire system [21]. Likewise, the implementation of a product platform enables the creation of a standard base from which to derive multiple variants, thereby optimizing resources and development time [22]. These features are especially relevant in research environments, where experimental objectives frequently change and available resources are limited.

This work proposes the application of a conceptual design methodology based on functions related to the user’s needs (customer-needs-driven design) for the development of a functional prototype of a cuttings growth chamber with modular architecture and a product platform. The methodological proposal suggested by Stone et al. (2008) [22] is similar to the logical framework design methods mentioned by Cross (2002), as it covers design aspects such as the establishment of functions, the determination of characteristics, and the generation of alternatives [23] but further allows for the development of a scalable platform that enables the integration of lighting or irrigation systems, sensors, or other components in the form of modules according to the needs of each investigation.

The primary scientific contribution of this work lies in demonstrating how a structured design methodology can be applied in experimental agricultural development contexts to create scalable, adaptable, and low-cost solutions that enable the optimal development of technology for this industry. This will lay the groundwork for future technology transfer to educational institutions, rural communities, or laboratories with limited resources, where this type of device can serve as a functional and accessible tool for innovation and experimentation. This will enable technological development to focus on the propagation and cultivation of plants, not only for green leafy vegetables or food in general but also for the cultivation of plants in danger of extinction or those with medicinal value whose cultivation in controlled environments has proven beneficial [24].

2. Theoretical Framework and Fundamentals of Modular Design

Figure 1 summarizes the conceptual transition that motivates this work, from the environmental and economic limitations of conventional agriculture, through the rise of controlled-environment techniques, towards the development of low-cost modular systems that democratize access to plant experimentation.

2.1. Design of Functional Prototypes in Applied Research

In agricultural research contexts, particularly in experimental plant propagation studies, technological resources are often limited. This constraint has driven the creation of low-cost, functional prototypes, which, while allowing the execution of specific experiments, frequently lack scalability and adaptability [18]. These prototypes are built using an ad hoc approach, which is defined in the Cambridge Dictionary as, “made or happening only for a particular purpose or need” [25]. Consequently, prototypes designed with an ad hoc approach are intended to quickly solve specific problems without considering potential reuse in future applications outside their original purpose.

Unlike commercial products with long development cycles, in the academic and experimental environment, it is necessary to implement methodologies that accelerate the design process without compromising functionality or the possibility of evolution. Hence, the importance of applying systematic strategies, such as structured conceptual design, from the early stages is crucial, particularly when a single system is expected to adapt flexibly to diverse experimental needs [26].

2.2. Product Platforms: Definition and Applications

A product platform comprises standard components, interfaces, and parameters that serve as the basis for developing a family of derivative products [15]. This approach has been widely adopted in industrial sectors, such as the automotive, household appliance, and electronic device industries, enabling cost optimization, reduced development times, and improved adaptability to diverse market needs [27].

In the field of experimental design, especially regarding agriculture or biological engineering, this approach is rarely adopted. However, its potential is considerable, as it enables the construction of a common technological base to which modules can be added, modified, or substituted, depending on the variables studied [22]. Thus, a single growth chamber, for example, could be adapted to different types of irrigation, lighting, or monitoring by simply reconfiguring its modular structure or adding extra components that allow functions to be adapted according to the needs and objectives of the experiments.

2.3. Modular Architecture: Principles and Applicability

Product architecture defines how a system’s functions are assigned to its physical components. According to Ulrich and Eppinger (2012) [16], there are two fundamental types: integral architecture, where functions are highly interconnected and components are difficult to modify, and modular architecture, where independent subsystems interact through well-defined interfaces.

Modular design offers crucial advantages for technological evolution, as it facilitates maintenance, allows for partial upgrades without requiring the redesign of the entire system, and promotes the reuse of components [21,28]. In design engineering, module identification heuristics (dominant flow, branched flow, conversion–transmission) have been used to transform functional models into physical architectures, supporting technical decisions from the early stages [17].

A modular architecture would not only optimize the design from a technical perspective but also establish a strong technology transfer component, allowing modular prototypes to be easily disassembled, adapted, and rebuilt by different users, thereby fostering local know-how and technological innovation.

2.4. Applicability in Agricultural Systems and Experimentation

Despite the wide range of modular design applications in various areas, such as the mechanical, electrical, electronic, design, and architecture sectors, its adoption in the agro-industrial or agricultural fields is limited and carried out indirectly [20]. This limitation is even more pronounced when considering an experimental approach. Some studies, such as those of Nikolov et al. (2023) and Wright et al. (2023) [13,14], have addressed the systematic design of growth chambers or agricultural devices through formal methodological strategies of experimentation, where low-cost culture chambers integrating monitoring sensors were designed. These experiments are similar to the those in the work of Bua et al. (2023), which is based on implementing and testing the correct functioning of modular monitoring components in a hydroponic greenhouse [10], and Fue et al. (2020), who focused on the component architecture of a robot dedicated to cotton harvesting [12]. However, these methodologies are not guided by the design area, thus lacking the benefits of applying a platform or modularization methodology to the experimentation and research process.

This shortcoming represents an opportunity for innovation. The development of modular prototypes for controlled agricultural systems would not only facilitate experimentation with different variables (temperature, humidity, time, and type of lighting or irrigation) but also allow adapting these same devices to the latest needs and experimental objectives with a minimal additional investment of time, effort, and even money. In addition, by applying a design methodology focused on user needs (as proposed by Stone et al., 2008 [22]), the relevance and applicability of the product are strengthened, aligning the methodology with the real needs of researchers, technicians, or students in low-budget contexts.

The present work aims to address this methodological gap by demonstrating how conceptual design based on modular platforms and architectures can serve as a strategic solution for developing functional prototypes in experimental agricultural environments. This approach is expected to not only improve the efficiency of the design process but also promote scalability, technology transfer, and democratic access to useful technologies for plant experimentation.

Recent open-source initiatives such as FarmBot [29] and MIT’s OpenAG Personal Food Computer [30] have explored the use of modular cultivation platforms for experimental agriculture, reinforcing the relevance of the methodological approach proposed in this work. FarmBot, an open-source CNC farming robot, enables configurable, automated irrigation, planting, and monitoring—highlighting scalability, repeatability, and modularity for research applications [29]. Similarly, the Open Agriculture Initiative has developed modular “Food Computers” that support decentralized, sensor-driven experimentation in controlled environments [30,31]. These examples underscore a growing interest in scalable, adaptable systems that closely align with the aims and approach of the present work, while demonstrating the practical viability of applying systematic design methodologies to agricultural experimental devices.

3. Materials and Methods

This study adopts a conceptual design methodology focused on user needs for the development of a modular product platform applicable to a cuttings growth chamber. The approach is based on the methodological framework proposed by Stoneet et al. (2008) [22], which enables the translation of customer needs into functional models and scalable product architectures. The methodology is developed in three main phases—needs analysis, functional modeling, and product architecture definition, in addition to the generation of a conceptual design employing an approximate geometric layout of the actual case study. This allows for exploration of the possible organizations of the modules, thus obtaining a better understanding of how the conceptual design will be physically represented in the near future [32].

3.1. Sample Selection Criteria

Since this study is oriented towards the exploration and application of a design methodology focused on user needs for the development of a modular cuttings growth chamber product platform, the selection of participants was based on the following criteria:

• Relevance of the participant’s knowledge, i.e., individuals with experience or knowledge in areas related to controlled agriculture, system automation, product design, or applied engineering were selected.
• Diversity of contexts of use, seeking to include participants who represented different application environments for the planned system, such as educational, small-scale urban, or academic environments.
• Critical perspective and reflective capacity were prioritized in the inclusion of people capable of critically evaluating design proposals and participating in technical co-creation or technical discussion processes to facilitate an exchange of high-quality ideas that can improve design proposals.

These criteria, together with logistical limitations and access to more specific populations, resulted in the selection of only four people with varied knowledge and different approaches, but who converged on a common point: the research. Each contributed relevant and specific information that enriched the needs analysis, which is the basis of the methodology implemented in this research. Therefore, this number was sufficient for implementing the method, allowing for a detailed and coherent analysis of the data. The selection of participants (n = 4) was based on a focused expert elicitation strategy, targeting individuals with specific and direct experience in the development, use, or evaluation of controlled-environment propagation systems. The selected participants were professionals with several years of practical experience in designing, developing, prototyping, monitoring, and automating plant growth chambers. Their profiles included postgraduate-level academic training and active involvement in research projects related to system implementation and testing. This small number of participants aligned with the methodological principles of conceptual design research. Several foundational studies support the use of small but expert samples in early-stage requirement elicitation: (i) Nielsen (1994) demonstrated that even as few as five users can yield the majority of insights during usability testing, highlighting that each user provides unique and valuable input [33]; (ii) Blessing and Chakrabarti (2009) [34] emphasized that a sample size of one person can be as valuable as a sample size of 1500 persons, depending on the type of study. Sample size can be as big or small as needed in any phase of development when the members possess contextual expertise or any characteristics that could influence the outcome of a particular study [34]; and (iii) Otto and Wood (2001) advocated for expert elicitation, where knowledgeable individuals inform functional modeling and requirement capture [26]. Therefore, a cohort of four expert participants in this study is consistent with accepted practices in design methodology and ensures a high-quality, focused input base for the functional development of the prototype.

3.2. Methodological Approach Adopted

The methodology employed adhered to the logic of structured conceptual design, which aims to transform qualitative information from the user into technical representations of the product. This approach consists of three fundamental steps [22]:

• Identification and categorization of user needs.
• Development of functional models (base and variants).
• Construction of a product architecture compatible with a modular platform.

This methodology has been successfully applied to consumer products, including coffee makers, electronic equipment, and portable tools. However, its implementation in low-budget experimental devices, such as those used in controlled agriculture, represents a relatively unexplored field. The incorporation of this approach into agricultural systems could enhance design efficiency, facilitate the development of prototypes, and enable adaptation to various experimental requirements at reduced costs [16,18].

The conceptual design process in this study was guided by a set of general design objectives that reflect the practical needs of controlled-environment agricultural systems. These objectives included: (i) achieving functional modularity, enabling individual subsystems to be independently developed and replaced; (ii) supporting scalability, allowing the system to be adapted to diverse experimental needs without full redesign; (iii) ensuring low-cost construction using widely available materials and components; and (iv) promoting user accessibility and ease of assembly, such that researchers with limited technical resources can replicate or adapt the system. Although this article focuses on conceptual and methodological development, these objectives shaped the architecture and functional decomposition of the system. Other specific technical objectives, such as energy optimization, lighting control, or environmental precision, are reserved for subsequent validation in the physical implementation phase. Similarly, the validation of conceptual design objectives, such as functional modeling and modular architecture, in later stages, including functional modularity, scalability, low-cost construction, physical integration, and interface standardization, are reserved for further work.

3.2.1. Analysis of User Requirements

The first stage involved identifying the functional needs necessary for the development of the prototype. For this purpose, semi-structured interviews were conducted with a group of four researchers who are potential users of the system, and they provided 55 needs related to the operation, monitoring, flexibility, and physical environment of the growth cabin.

Each need was evaluated in two dimensions:

• Frequency of mention among interviewees;
• Weight assigned (from 0 to 5) according to perceived importance.

Each part of the evaluation was addressed individually so that none of the users were aware of the other’s responses, thereby preventing communication from influencing individual responses.

A quantitative table was then drawn up to classify the needs according to their weight and the frequency with which they were mentioned.

• Base needs: mostly those with a weight of ≥3 and a frequency of ≤(n/4), (where n = number of researchers interviewed), which, according to the methodology, are the leading candidates for base needs.
• Differentiating requirements: requirements that do not meet the above-mentioned weight and frequency classification that serve as auxiliary or specific functions useful for variants.

Following this quantitative analysis, a complementary qualitative review was conducted to identify redundancies and group-related needs and to eliminate those that were not necessary for the system’s basic operation. From this rigorous analysis, 26 basic needs were identified, thus allowing for the construction of a solid base for the development of the functional model and the system modules.

3.2.2. Functional Modeling of the System

In this phase, functional models were developed using the approach proposed by Stone and Wood (2000) [32] based on energy, material, and signal flows [35]. The following structured sequence was employed.

4.

Black box model: inputs (energy, water, and data) and outputs (rooted cuttings and monitoring data) were represented.

5.

Function chains: sequential and parallel subfunctions within the system were detailed.

6.

Complete functional model: all basic functions were integrated into a single diagram, visualizing relationships and possible modularity points.

Subsequently, two variant functional models were developed, incorporating differentiating functions such as different types of irrigation (aeroponic, drip) and specific sensors. These variants demonstrated that the base functional model was robust and scalable enough to support additions without compromising the overall system architecture.

3.2.3. Product Architecture Definition

To build a modular architecture consistent with the functional model, three heuristics from the methodology proposed by Stone [17,22] were applied:

• Dominant flow heuristics: linear trajectories of functions from the input to the output of the system were identified.
• Branched flow heuristics: independent functional modules connected by shared flows were recognized.
• Conversion–transmission heuristics: modules where transformations occur (e.g., electrical to mechanical energy) were isolated.

These heuristics made it possible to identify recurring modules such as the

• Irrigation system;
• Lighting module;
• Cuttings growth module;
• Power supply module;
• Heating or air conditioning system;
• Environmental variables monitoring unit.

The modules were represented in block diagrams, considering possible spatial configurations, interfaces, and future expansions. The possibility of implementing hybrid architectures (slot and bus type) was evaluated to facilitate the insertion of new components.

3.3. Application of the Methodology to the Case: Cuttings Growth Chamber

Once the methodology was established, it was applied to the specific case of a growth chamber for hydroponically propagated cuttings. This device was designed to enable the controlled rooting of plant cuttings under laboratory or greenhouse conditions, integrating functions for irrigation, thermal control, lighting, and environmental monitoring.

We proceeded as follows:

• Based on the needs analysis, key components such as root and stem humidity sensors, temperature sensors, photoperiod control, water pumps, and data storage units were prioritized.
• In the variant functional models, aeroponic and drip irrigation systems were specified as examples of design adaptability.
• The resulting architecture enabled the visualization of a central structure (platform) with interchangeable modules, depending on the type of experiment.
• Two conceptual design diagrams were developed, showing possible physical configurations of the prototype.

This case study demonstrates that a structured design methodology can be applied effectively in the development of low-cost agricultural devices, resulting in modular, scalable, and adaptable prototypes. The result not only responds to a specific need but also establishes a methodological basis that can be replicated in other contexts.

3.4. Other Methodologies Considered

The use of design methodologies specifically applied to agricultural design is identified primarily in the development of modular machinery, as Suñer concludes in her thesis [20]. If used, these are generally employed to develop modular architectures for the experimentation with or production of specific plants. However, the possibility of turning this design into a product platform that can be used, modified, and reused as needed is not taken into account. Such considerations would benefit users in terms of time savings and the costs of subsequent developments by favoring the reuse of parts or modules of a first robust design in different subsequent redesigns, without the need for a rigorous redesign.

This is the main interest in the development of a functional prototype of a cutting growth chamber, where modularity and scalability allow for conducting different experiments with fluctuating variables, without requiring the redesign and complete reconstruction of the chamber. Instead, only minor modifications are necessary to achieve the different objectives.

For this reason, a literature review was carried out to select the design methodology from the following:

• TRIZ, translated from Russian, means the “theory of inventive problem-solving”. It is a methodology for designing and developing product families based on product platforms. Although it aims to generate innovative solutions, it does not provide modularity or scalability strategies; instead, it focuses on identifying and resolving design contradictions. For example, increasing the strength of a material may imply an increase in weight, which can be counterproductive [36]. Therefore, this method was eliminated.
• Gershenson proposes a modular design methodology and even suggests a method for determining the modularity relationship. However, this method is aimed more at redesign than at creating a design from scratch. Nevertheless, it was not ruled out as a helpful tool that could be used in the future [37,38].
• Finally, the methodology used in this paper was proposed by Stone, which encompasses the design process based on customer needs, product platform design, and modular design [17,22,32]. These last two points make it especially useful for scalable designs, allowing for, if necessary, the integration of modules, components, or even software without conflicts. This would also allow the same design to expand its functions, without requiring a redesign from scratch, and to be used in different contexts.

4. Results

4.1. Results of the Needs Analysis

The first phase of prototype development involved identifying and classifying the system’s functional requirements. For this purpose, semi-structured interviews were conducted with the group of four users, who provided a total of 55 needs related to the operation, monitoring, flexibility, and environmental conditions of a growth chamber for cuttings used in experimentation.

Each need was evaluated according to two criteria:

• Frequency of occurrence, i.e., the number of participants who mentioned the need;
• Relative weight assigned, i.e., the importance perceived by the user, on a scale from 0 (not important at all) to 5 (very important).

These data were systematized and tabulated in

Table A1. Initial list of 55 prototype requirements identified through user interviews. Each need is accompanied by its frequency of mention (frequency) and its perceived importance (weight, 0–5 scale). This dataset served as the foundation for the quantitative and qualitative classification processes described in Section 4.1.

Needs	Frequency	Weight
Irrigation time control	2	4
Monitoring of light power in the stem area	4	3.5
UV light monitoring in the stem area	1	2
Humidity monitoring in the stem area	3	4
Humidity monitoring in the root area	2	4
Temperature monitoring in the root area	3	3.5
Temperature monitoring in the stem area	4	4
Monitoring of pH in the water	2	3.5
Electrical conductivity monitoring in roots	1	2.25
Water flow control	1	3.5
Control of light power	1	3.75
Photoperiod control	2	4
Warning of non-functional components	1	4
Programming of sensors and activators	1	4.75
Remote monitoring	1	3.75
Cameras (photo/video)	2	3
Internal thermal sensation monitoring	1	3
Water level sensor in water container	2	2.75
Visualization of foliage density	1	3
Hydroponic-type drip irrigation	3	2.75
Hydroponic-type ebb–flow irrigation	1	2.5
Aeroponic-type irrigation	3	3.75
Water pumps	2	4.75
Irrigation piping	3	4.5
Sprayers	1	4.75
Pre-calibrated sprayers	1	3.75
Fogger in the root area	1	3.5
Fogger in the stem area	1	2.5
Irrigation drainage	1	4.25
Regular water filter	2	3.75
“Natural” water filter (layers of materials that filter the water)	1	3
Water spray to stems	1	2
Fans/extractors to regulate air and temperature	3	4.25
Heating	3	4
Cooling	3	4.25
Air filters	1	2.5
RGB LED lights	3	4.25
UV-type lights	1	3
Fluorescent lights	1	1.75
Water container for irrigation	4	4.75
Drainage container for irrigated water	1	3.5
Tanks with nutrients (minerals)	1	3.5
Storage of data collected from monitoring	2	4.5
Space for a few cuttings	1	3.25
Space for many cuttings	1	3.75
Easy and complete access to cuttings	2	4.25
Stem growth area	2	4.5
Root growth area	1	4.5
Space for internal wiring	2	4
Space for boards and circuits	1	4
Isolation from outside temperature	1	4.5
Isolation from external light	2	4
Assembled/disassembled structure	1	3.5
Support for cuttings	2	4.75
Outside temperature monitoring	1	3.75

in Appendix A, which presents the complete list of needs, together with their frequency of mention and the average weight assigned by the interviewees. From this table, a scatter plot was constructed (see Figure 2). The scatter plot illustrates 55 identified user needs, evaluated by frequency (x-axis) and weight of perceived importance (y-axis, scale 0–5). Following the methods of Stone et al. (2008) [22], needs classified as “basic requirements” (essential to the core functionality of the platform) are those meeting the base criterion (weight ≥ 3, frequency ≤ 1), which appear in the upper left quadrant of the scatter plot. Although a significant concentration of core needs falls within this preliminary classification, a qualitative refinement, detailed in Section 4.1, enables the inclusion of additional interconnected needs, thereby ensuring a solid foundation for the modular platform.

The result was a final selection of 26 base needs, representing a 52.72% reduction, without compromising system functionality, thereby optimizing resources to focus on the requirements essential for the platform design. (see Appendix A,

Table A2. Final selection of 26 base needs derived from the initial set. These requirements were filtered through quantitative and qualitative criteria, prioritizing those essential for the functional operation of the modular platform. The table includes frequency of mention and perceived importance weight for each need, as identified in user interviews.

Platform Needs	Frequency	Weight
Irrigation time control	2	4
Monitoring of light power in stem area	4	3.5
Programming of sensors and activators	1	4.75
Internal thermal sensation monitoring	1	3
Stem growth area	2	4.5
Root growth area	1	4.5
Isolation from outside temperature	1	4.5
Assembled/disassembled structure	1	3.5
Support for cuttings	2	4.75
Outside temperature monitoring	1	3.75
Space for boards and circuits	1	4
Photoperiod control	2	4
Irrigation time control	2	4
Water pumps	2	4.75
Fans/extractors to regulate air and temperature	3	4.25
Cooling	3	4.25
Heating	3	4
Irrigation piping	3	4.5
Water level sensor in water container	2	2.75
Water container for irrigation	4	4.75
Irrigation drainage	1	4.25
Water container for irrigation	4	4.75
Storage of data collected from monitoring	2	4.5
Easy and complete access to cuttings	2	4.25
Space for internal wiring	2	4
Isolation from external light	2	4

, for a list of the selected base needs). These basic needs are also identified in the scatter plot (see Figure 2).

These needs are considered essential and represent the functional core of the product platform. The remaining requirements were classified as differentiating requirements applicable to the design of future variants. It is this debugging and classification that allowed us to improve the clarity of the functional model and to anticipate combinations and dependencies between requirements.

For example:

• The need to monitor internal temperature also implies controlling the airflow and ensuring adequate thermal insulation.
• The implementation of different irrigation systems, such as drip or aeroponic types, requires an open and modular architecture in the hydraulic components.

Overall, this needs analysis laid the groundwork for the subsequent phases of development by defining both mandatory platform constraints and possible modular extensions to accommodate future experimental requirements.

4.2. Functional Modeling: Basis and Variants

Following the base needs analysis (Table A2), the functional model of the product platform was developed using the functional decomposition methodology proposed by Stone and Wood (2000) [32] and Ulrich and Eppinger (2012) [16]. This methodology involves representing a product through its elementary functions, structured in the form of logical chains that describe the flow of energy, materials, and signals within the system. These flows are represented by colors, which remain unchanged unless a flow transformation occurs, e.g., an electrical energy flow transformed into a signal (see Figure 3 and Figure 4).

The functional modeling process was structured in three levels, which will be described below.

4.2.1. Basic Functional Model

We began with a black box model, which defines the inputs and outputs of the system, without detailing the internal components that correspond to the 26 base requirements. This scheme shows the inputs of energy, water, and user commands, as well as the expected outputs: rooted cuttings, measured data, and outputs of the components that achieve the controlled environmental conditions. The resulting black box model is shown in Figure 3.

From this model, the detailed functional model was constructed (see Figure 4), where the inputs are connected through specific sub-functions to achieve the desired outputs. The functions are organized in sequential and parallel chains, according to the operational logic of the system, by developing sub-functions that would allow for “regulating temperature”, “hydrating roots”, “illuminating stems”, and “monitoring variables”, among others. The interaction between these functions reflects a potential modular architecture, allowing for the identification and functional flows within the system at a later stage. It should be noted that, although the definition changes in several of these subfunctions, the type of flow represented does not change. For example, when electricity is transformed into light, each remains a flow of energy.

4.2.2. Variant Functional Models

Based on the base functional model and the integration of differentiating needs (Table A1 and Table A2), two additional functional models were developed, representing variants of the prototype with expanded components or functions (see Figure 5). These variants incorporate alternative irrigation systems and specific sensors that are not part of the base model.

• Variant 1 incorporates aeroponic irrigation, humidity sensors, and a pH sensor integrated into the water container.
• Variant 2 replaces the aeroponic irrigation system with a drip irrigation system, while maintaining the same sensors.

In variant 1 (see Figure 5), the water flow is dispersed by sprinklers located in the root zone. A recirculation cycle returning water back to the tank is also included.

In variant 2 (see Figure 6), water flows via gravity from the stem to the roots. The system features mechanisms for collecting and returning water to the reservoir, thereby emulating a closed cycle and facilitating water recirculation.

Both variants retain the structure of the base functional model, thus validating its robustness and consistency. Additional modules are integrated, without altering the primary functions of the system, demonstrating that the proposed architecture is indeed modular and scalable.

4.3. Application of Heuristics: Module Identification

With the base functional model and its variants clearly defined, a series of architectural design heuristics were applied to identify the functional modules of the system. These heuristics facilitate the transformation of the logical representation of functions into a proposed modular physical structure, adhering to the principles of flexibility, independence, and scalability.

The methodology used follows the approach described by Stone and Wood (2000) [32], where three main heuristics are applied:

• Dominant Flow Heuristic: identifies linear sequences of functions through which the same inputs (water, energy, signal) flow, without bifurcations.
• Branching Flow Heuristic: detects functional branches that can operate as independent subsystems connected at a common point.
• Conversion–Transmission Heuristic: highlights sub-functions in which transformations occur from one type of flow to another (e.g., electrical energy to mechanical energy), which generally defines natural boundaries between modules.

These heuristics were applied to both the base functional model and its variants, yielding consistent results that demonstrate the feasibility of implementing a modular architecture without compromising the system’s functionality.

4.3.1. Application in the Functional Model Variant with Aeroponic Irrigation

In Figure 5, the sub-functions associated with the aeroponic irrigation system are grouped based on the following:

• Dominant flow, i.e., the water supply for the irrigation, electric power, air conditioning module, and support and growth area for cuttings.
• Conversion–transformation, i.e., using electrical energy for lighting, heat, and water pressure.
• Functional bifurcation, i.e., the supply of electrical power to the components that require it (including sensors) and the physical division of the cuttings growth area.

In addition, specific components were identified for

• Environmental monitoring using sensors for humidity, temperature, and pH levels.
• Control and programming, including actuators and microprocessing units.

This modular grouping allows a physical implementation in which each of these subsystems can be assembled, upgraded, or replaced independently, as long as the defined interfaces are respected.

4.3.2. Application in the Functional Model Variant with Drip Irrigation

In Figure 6, the modular structure is similar to that in the previous case, but the internal configuration of the irrigation module alters as the water flow trajectory changes from the stem zone to the root and then back to the collection area for water recirculation; this could constitute a secondary module for water management.

The remaining modules (lighting, growth area, heating, monitoring, and control) remain unchanged, providing evidence for the stability of the proposed platform architecture.

4.3.3. Comparison with the Baseline Functional Model

Figure 7 illustrates that when applying the heuristics to the base model, significant coincidences are observed with the modules detected in the previous variant models. Stone (2008) [22] mentions that when different heuristics converge on the same set of functions, the validity of that grouping as an autonomous module is reinforced. In this case, modules that fulfill functions such as irrigation, lighting, and thermal air conditioning, among others, appear repeatedly, confirming them as stable modular cores.

4.3.4. Physical Architecture Considerations

Based on the results obtained, it was feasible to implement a modular slot-type architecture in which each module is directly connected to a standard base structure. A bus architecture is also considered especially useful for handling the interconnection of sensors and control systems through shared protocols [16].

This phase allowed us to consolidate a preliminary physical structure of the system, in which

• Each module can be developed and evaluated independently of the others.
• Technological upgrading is facilitated via components.
• Prototype customization is enabled according to the objectives of each experiment.

4.4. Conceptual Design: Block Diagrams

Once the modules were identified through functional analysis and the application of heuristics, we proceeded to construct the conceptual design of the cutting growth chamber. This stage translates the functional and modular architecture into a preliminary geometric and spatial proposal, usually represented by block diagrams that allow for the visualization of the

• relative arrangement of the modules within the system;
• possible physical and logical interfaces between components;
• energy, material, and signal flow paths.

This representation does not yet imply a detailed CAD-level design or 3D modeling, but it lays the groundwork for later stages of design and physical implementation.

4.4.1. Proposed Modular Layout

Based on the variant functional models discussed in Section 4.3, two different conceptual design configurations were proposed, based on variant 1.

In the design illustrated in Figure 8, the following can be observed:

• The aeroponic irrigation module is positioned in a peripheral location to minimize interference with other subsystems while maintaining water recirculation.
• The control and programming module is housed on the external side, allowing direct access to microcontrollers and user panels.
• The environmental monitoring module is distributed between the root zone and the stem zone, with strategically embedded sensors.
• The lighting system is mounted at the top and oriented towards the area of leaf growth.
• The air conditioning system and the power supply module are positioned at the top to prevent contact with water.

This configuration optimizes the gravitational flow of water in the recirculation system, allowing the sensors to be located close to the measurement point, without the need for extensive wiring.

In the design illustrated in Figure 9, the following can be observed:

• The growth area is directly connected only to the lighting module.
• The irrigation module remains the same, except for its position, as it is located to the side of the growing area, and the air conditioning system is placed above it.
• The location of the lighting and control module is maintained.
• The sensors are distributed similarly but with an emphasis on measuring the impact of irrigation in the designated zone.

This configuration enables the adaptation of the same general cabin chassis to a different operating logic, without modifying the main structural modules. This reaffirms the effectiveness of the modular platform approach because, without the application of a modular design, this type of organization and reorganization of modules and components would not be possible.

While both irrigation systems (aeroponic and drip) were explored as part of the functional modeling process in Section 4.3, the aeroponic system was selected as the basis for the block diagrams, owing to its stronger alignment with the modularity principles. Aeroponic systems often support a tighter integration of sensors and digital controls, thanks to their compact misting environment and reduced complexity in the fluid pathways [39,40]. These characteristics facilitate subsystem independence and interface clarity, in line with the design objectives outlined in Section 3.2. As this work remains at the conceptual stage, it does not imply empirical superiority, and a quantitative comparison of performance awaits pending comprehensive physical testing.

4.4.2. Design Implementation Considerations

Although the block diagrams presented above are abstract representations, they serve several key functions:

• They facilitate subsequent 3D modeling by establishing spatial relationships between modules;
• They clarify the components and their possible behavior to anticipate assembly, maintenance, and access problems;
• They allow for the exploration and comparison of design variants quickly before building a physical or digital prototype.

In the future, these diagrams can be converted into physical drawings, CAD models, or virtual simulations, integrating materials, tolerances, and mechanical and electrical interfaces.

It is suggested that the digital implementation start with parametric 3D modeling, where each module is defined as an independent entity but is connectable through a standard system of slots or ports.

4.4.3. Value of Conceptual Design in the Process

Conceptual design bridges the gap between abstract functions and physical structure, in addition to

• reducing downstream integration errors;
• serving as a tool for technical communication between designers and potential users.

In this case, the conceptual design confirms that the defined modules are not only functional but also physically integrable within a logical and reusable architecture. This positions the prototype as a scalable, replicable solution with the potential for transfer to other agricultural experimental contexts.

4.5. Modular Synthesis of the Proposed Design

In conclusion, an overview of the designed system is presented, which synthesizes all the identified functional modules and their interactions. This overview facilitates an understanding of the system as a whole and provides a basis for future physical implementations and experimental adaptations (see Figure 10).

5. Discussion

The results obtained demonstrate that the design methodology, utilizing product platforms and modular architecture commonly used in industrial sectors, can be effectively adapted for the development of experimental agricultural prototypes. The identification of 26 base needs from the initial 55 using quantitative criteria (weight ≥ 3, frequency ≤ n/4) and complementary qualitative analysis proves the robustness of the filtering process proposed by Stone et al. (2008) [22]. This 53% reduction in initial requirements, without compromising system functionality, indicates that the methodology allows teams to channel their resources into the features with the greatest functional return.

The successful development of variant functional models (aeroponic and drip) that retain the base structure confirms the architectural stability of the proposed modular design. This feature is particularly valuable in agricultural research contexts, where experimental objectives change frequently, and resources are limited. The ability to adapt the system by replacing or adding specific modules, without requiring a complete redesign, represents a significant advantage over the ad hoc approaches prevalent in the reviewed literature.

Unlike Wright et al. (2023) [14] and Nikolov et al. (2023) [13], who developed low-cost cameras with fixed structures, this study provides a methodology that prioritizes adaptability and scalability. While previous approaches address specific needs, the modular approach enables multiple configurations to be derived from a common platform, thereby optimizing the initial investment, facilitating technology transfer, and addressing varying needs.

The absence of structured methodologies in experimental agricultural design, as identified by Suñer (2017) [20], is directly addressed through the systematic application of modular identification heuristics. The identified modules (irrigation, lighting, monitoring, control) emerge consistently in both variants, validating the universality of these functional components in controlled cropping systems.

Quantitative needs analysis, functional modeling, and validation through heuristics strengthen the credibility of the results. Specifically, the application of the three design heuristics provides multiple perspectives for modular identification, increasing the robustness of the process. The convergence of the results among different heuristics strengthens the validity of the proposed modular groupings. In addition, the use of block diagrams as a bridge between function and form facilitates technical communication and reduces interpretation errors in later phases of implementation.

The results suggest that implementing the modular design in experimentally controlled agriculture can democratize access to controlled cultivation technologies. The ability to reuse modules in different experiments would significantly reduce development and implementation costs for academic institutions and research centers with limited budgets and could enable the sharing of specific modules between research centers, without requiring complete systems. This could accelerate innovation in controlled agriculture and promote the standardization of interfaces between developments.

On the other hand, the study exhibits some limitations. The small sample size (n = 4 participants) may limit the generalizability of the findings; however, this limitation is mitigated by the homogeneity of the profiles and the qualitative approach adopted. The lack of physical validation of the prototype prevents the evaluation of its operational aspects, such as energy efficiency, interface durability, or module compatibility, under real-world conditions. Additionally, specific economic aspects such as manufacturing costs and return on investment, which are critical for adoption in resource-limited contexts, were not evaluated.

However, these limitations do not constitute failures but rather future stages that we intend to develop in subsequent works, focusing more on targeted research. From this starting point, a research and development agenda is established that will enable progress toward implementing, technically validating, and optimizing the proposed design.

Methodological note: Although this article presents a structured and detailed conceptual development, including the construction of a functional prototype and initial operational tests (see Appendix B), it is essential to emphasize that the current stage remains exploratory. The physical validation presented here is limited to basic operation and environmental monitoring, without systematically testing the performance assumptions related to modularity, energy efficiency, or long-term stability. A follow-up study, in the form of a technical note, is currently being prepared to evaluate these aspects under controlled experimental conditions. This staged approach allows for a deeper exploration of modular reconfiguration and optimization in subsequent research phases.

6. Conclusions

This study successfully demonstrated that industrial design methodologies based on product platforms and modular architecture can be effectively adapted for the development of experimental agricultural prototypes. Applying the methodology of Stone et al. (2008) [22] yielded a robust, modular system that strikes a balance between functionality, scalability, and adaptability.

The systematic identification of 26 basic needs and their translation into coherent functional modules (irrigation, lighting, monitoring, control, thermal conditioning) enabled the establishment of a replicable framework for the development of other experimental agricultural systems. The stability of the proposed architecture in response to specific modifications was validated through structured-functional consistency in variant models.

This research contributes to the methodological gap identified in the design of experimental agricultural prototypes by providing a structured process, ranging from needs analysis to physical conceptualization. The combination of quantitative and qualitative analysis for needs classification, together with the application of multiple heuristics for modular identification, represents a methodological innovation adapted to the agricultural context.

The use of block diagrams as a conceptual design tool eases the transition between functional modeling and physical implementation, simplifying the development of multifunctional systems.

The findings position modular design as a viable strategy for democratizing access to experimental agricultural technologies. The ability to adapt a base system to multiple experimental configurations through interchangeable modules can reduce economic barriers for educational institutions and research centers in developing countries.

The proposed methodology extends beyond the specific case of growth chambers, establishing principles that are applicable to the design of other agricultural systems, such as modular greenhouses, automated irrigation systems, or environmental monitoring devices.

The study is limited to a conceptual design without physical validation, which prevents the evaluation of critical operational aspects such as energy efficiency, interface durability, or system behavior under real conditions. The small sample size, although methodologically justified, limits the generalization of the identified needs.

We aim to transition to the physical implementation of the prototype through digital fabrication techniques and parametric modeling, followed by a comparative experimental evaluation of the modular variants. Exploring applications in other crops, such as medicinal plants and endangered species, would broaden the impact of the proposed methodology. Future research will also aim to incorporate economic analysis, evaluate transferability to various contexts, and establish a standard interface for this approach.

Finally, although this article focused on the conceptual development and methodological foundation of a modular growth chamber system, it is important to note that a physical prototype was constructed and tested under controlled conditions. A summary of these preliminary functional validations—including environmental control metrics and basic operational behavior—is presented in Appendix B. A more detailed description of the prototype’s physical structure, components, and implementation strategy is currently under preparation and will be submitted as a separate technical note focused on technology transfer.