Evaluating urban mini plant factories: engineering and software cost perspectives for agriculture sustainability

Precision agriculture serves as a crucial solution to address the challenges of food security and agriculture sustainability. It provides significant benefits and enables effective monitoring of agriculture throughout the entire supply chain using a technology-driven approach that prioritizes internet connectivity and leverages big data. A noteworthy application of precision agriculture is the Mini Plant Factory, where plants are cultivated in closed systems with artificial lighting. The development of the Mini Plant Factory requires an economic feasibility analysis as a determinant of technology viability before widespread implementation, aiming to promote agriculture sustainability. The Software Cost Estimation Model (SCEM) assesses software development’s cost and time requirements. Engineering economics evaluates economic feasibility using metrics like NPV, BCR, IRR, PBP, and BEP methods. Function Point Analysis for UMPF software development estimates a cost of Rp 33,052,414, equivalent to 1,311 man-hours. Investment feasibility analysis considers the utilization of UMPF as viable, with an NPV of IDR 46,938,615, BCR of 1.08, IRR of 28%, and projected profitability within four years. Precision agriculture strategically addresses complex agricultural challenges, fostering sustainability with innovations such as the UMPF. The SCEM and engineering economics confirm the feasibility and rationale for implementing UMPF technology, providing essential insights for sustainable agricultural practices.


Introduction
The Food and Agriculture Organization (FAO) anticipates that the global population will reach 9.6 billion by 2050.This population increase may result in aging farmers, where a significant portion of agricultural labor comprises older generations.This is primarily due to the need for more enthusiasm among younger generations to pursue careers in agriculture.The population growth has led to the depreciation of agricultural land over the past decade [1].According to data from the Indonesian Statistical Agency in 2015, the total agricultural land area was 8.06 million hectares [2].The insufficient availability of high-quality agricultural land has led Indonesia to import a significant amount of food, including vegetables Furthermore, unpredictable weather conditions challenge farmers to cultivate food crops.Despite these challenges, the agricultural sector has demonstrated promising economic performance, as indicated by data from the Central Statistics Agency (BPS) in 2021, reporting a 13.28% increase in Indonesia's economy, even during the COVID-19 pandemic.[3].
Farmers require technology capable of providing information on sustainable food cultivation needs unaffected by weather or environmental conditions.This necessity emphasizes the importance of investing in precision farming technologies.Precision farming involves various approaches, from simple to complex methods, emphasizing Internet connectivity and big data through the Internet of Things (IoT) [4,5].The advancement of precision farming has the potential to create efficient and sustainable agricultural practices.It aims to enhance crop cultivation efficiency and reduce economic losses, ensuring profitability even during low price fluctuations [5,6].A noteworthy advancement in precision agriculture technology is the introduction of the Plant Factory concept.This technology integration can benefit farmers by providing reliable information on food cultivation needs independent of weather or environmental influences.Implementing precision farming technologies can contribute to addressing the challenges posed by an aging farmer population, land depreciation, and the need for sustainable agriculture.This approach aligns with international efforts to promote agricultural development and food security.Therefore, advocating for adopting precision farming technologies is crucial for advancing sustainable and productive agriculture on a global scale.
Plant Factory is a confined growing system offering regulated environmental settings tailored for plant development, particularly on vegetables.It offers stable lighting, artificial temperature, and humidity control, ensuring optimal conditions regardless of external environmental factors [7].The monitoring system within a Plant Factory is crucial for its successful operation [8].However, developing Plant Factory systems, particularly monitoring and control, can be costly.This has led to the developing of smaller-scale plant factories known as Mini Plant Factory [9].Mini Plant Factories are more economical and versatile in size and portability.They are particularly suitable for urban areas with limited or poor-quality agricultural land, supporting urban farming to meet the vegetable demand in urban regions [10].The design and development of a Mini Plant Factory requires careful preparation for both hardware and software components.The success of implementing Mini Plant Factory technology depends on production costs and the revenue generated [11].Therefore, an indepth economic feasibility analysis, including software cost estimation, is essential for designing and developing an Urban Mini Plant Factory (UMPF).
The Software Cost Estimation Model (SCEM) considers the accuracy of estimates, including time, cost, quality, and activity planning.Measurement and quantification modeling are necessary to support effective software process management [12].Estimating software costs is a complex task that requires continuous and accurate calculations.It involves assessing the complexity of software development and determining the actual costs involved.Software cost estimation can be performed using methods such as Function Point Analysis (FPA), which measures software size based on functional requirements.Conducting an economic feasibility analysis is essential to assess the financial feasibility of a project.It helps identify whether an investment is financially viable and profitable when executed.Financial feasibility analysis can be conducted using various methods, including Net Present Value (NPV), Internal Rate of Return (IRR), Benefit Cost Ratio (BCR), Payback Period (PBP), and BEP.
This study aims to conduct a technical and economic analysis of the design and development of an UMPF device to determine its financial feasibility in crop production.Additionally, a software cost estimation model will be analyzed to develop the device's software, assessing the software development costs.

Framework
The UMPF device comprises an integrated sensor system connected to a cloud server and implements an IoT system.The project for designing and developing UMPF requires a technical, economic analysis.Technical, engineering economic analysis determines the initial cost estimates for the device's design and assesses the benefits gained after its implementation.The research framework is illustrated in Figure 1.
Figure 1 shows that in the design and development of UMPF, two calculation phases will be carried out: the SCEM calculation and the technical economic analysis calculation.The SCEM calculation determines the software development cost for UMPF device.The SCEM calculation method is the Function Point Analysis (FPA) method.This method is employed to assess the complexity weight of the software based on programmer evaluations.The complexity weight of the software is then used to determine the software development cost.Engineering economic analysis calculations are conducted to assess the financial feasibility of UMPF device's design and implementation.Additionally, engineering economic analysis determines the payback period for the initial investment and the profits obtained.The engineering economic analysis includes calculations for BCR, IRR, BEP, NPV and PBP.The UMPF consists of two main components: an iron frame rack for plant growth, installing a serial network for environmental control systems, and lighting control on the plant rack.One UMPF unit is designed with dimensions of 300 × 180 × 60 cm, featuring three levels used to place plant trays and LED light control.Each planting rack is lined with plywood and wire mesh to facilitate tray placement.The UMPF Monitoring System utilizes SmartAgri.idCloud Server as its software.Figure 2 depicts the UMPF.

Software Cost Estimation Model (SCEM)
Estimating software development costs is essential to allocate resources to attain project goals and objectives efficiently.This research employs the SCEM for software cost estimation, employing mathematical models or algorithms based on historical data and various factors that impact software costs and schedules [13].The SCEM analysis is performed within the framework of creating an application for overseeing the UMPF, covering both software and hardware elements.The method used in the SCEM is Function Point Analysis (FPA).The initial step involves distributing questionnaires to developers/programmers to assess 14 characteristics of Relative Complexity Adjustment Factor (RCAF) [14].The filled questionnaire results are utilized to calculate the RCAF value.Then, the analysts calculate CFP value by analyzing the Agrieye Cloud Server content.The CFP and RCAF values are used to calculate the Function Point (FP) value.Subsequently, the FP value is employed to calculate the Effort, KLOC, and Final Effort value and is then utilized to estimate the cost for the workforce based on the performed activities.The complexity weight value used to compute the CFP value is provided in Table 1.Subsequently, the equations for FP, KLOC, Final Effort, Effort, and Cost are represented by equations ( 1), ( 2), ( 3), ( 4), ( 5), and ( 6).

Table 1. The numerical representation of complexity weight
In The data process moves across the system's application boundary to deliver information to the user.4 5 7

Internal Logical Files (ILF)
A set of data logic confined within the application boundary, preserved as EI (External Input) and stored internally.External Inquiry (EQ) Data logic external to the application, overseen by other applications, is employed exclusively for reference purposes.

Engineering Economic Analysis
The engineering economic analysis used to evaluate the financial feasibility of implementing UMPF includes capital investment for materials and construction in the UMPF development.It encompasses operational costs as well as maintenance and repair costs for equipment.Additionally, it requires the production cost of vegetables using UMPF adjusted with existing market data.Production cost analysis in this research is conducted through two stages: Fixed and Variable Costs.The Fixed costs in this investigation encompass Depreciation Costs (D) and Maintenance and Repair Costs (PP); additionally, the overall fixed cost (FC) can be computed using the provided equation: In this context, D represents the depreciation in Indonesian Rupiah (IDR) for the asset, P denotes the equipment price per year in IDR, S signifies the final equipment price in IDR, and L represents the technical life of the assets in years.Additionally, PP represents the value of maintenance and repair in IDR, where P represents the equipment price per year in IDR, and 5% is the estimated percentage.Variable costs include electricity, raw material costs (Pak choi mustard seeds), growing media, nutrient requirements, and labor costs.Electricity usage is measured in kW (kilowatts) or W (watts).The electricity tariff is expressed in IDR/kWh, thus resulting in the electricity consumption rate in IDR/hour.Labor costs will be provided by the Regional Minimum Wage (UMR) and the specific job activities performed.The calculation of each variable cost is as follows: Where B1 is the cost of pak choi seeds (IDR per unit), the as is the amount of pak choi seeds needed (packs per unit), and the Hs is the price of pak choi seeds (IDR per pack).B2 is the cost of planting media (IDR per unit), the am is the amount of planting media needed (packs per unit), and the Hm is the price of planting media (IDR per pack).B3 is the cost of nutrients (IDR per unit), the an is the amount of nutrients needed (packs per unit), and the Hn is the price of nutrients (IDR per pack).This study is grounded in various assumptions and parameters, forming the basis for analysis and calculations.These assumptions and approaches are detailed in Table 2.The economic feasibility analysis is conducted using several methods including NPV, BCR, IRR, PBP (Payback Period), and BEP (Break-even Point).NPV calculates the difference between expenditures and future income [10].BCR is employed to determine the relationship between the cumulative present value of positive benefits and the present value of negative costs.IRR calculates the discount rate equal to or greater than the prevailing discount rate.IRR calculations require cost estimates to estimate the benefits of the investment activity.PBP is used to calculate the payback time of the initial investment in the planning stage.BEP determines where a business neither makes a profit nor incurs a loss.BEP represents the equilibrium between expenses and revenue.

Engineering Economic Analysis
The economic analysis is conducted by determining the production costs of microgreen Pak choi on the UMPF.The production costs consist of fixed cost and variable cost.Additionally, the calculation of investment and production costs is used to predict the income and expenses of microgreen Pak choi production on the UMPF over the economic life of the UMPF installation.The investment criteria parameters used for the economic feasibility analysis of the UMPF are NPV, BCR, IRR, PBP, and BEP.The results of the engineering economic analysis are summarized in Table 3, and the annual benefit distribution over the UMPF lifespan is illustrated in Figure 3.  Based on Table 3, The NPV value for using UMPF is IDR 46,938,615, indicating a positive value.The NPV value suggests that using UMPF generates significant income over the ten years.The BCR value obtained from the use of UMPF is 1.08.The BCR indicates that the BCR value is greater than 0, implying that the use of UMPF in the business can continue because it will generate profits over the ten years with an initial investment of IDR 42,053,170.The PBP calculation used is the annuity because the benefits obtained each year are equal.The PBP value obtained is 3.27 years.The breakeven point indicates that the business neither incurs losses nor gains.In the case of UMPF usage will reach its break-even point after approximately 3.38 years.
In conclusion, the UMPF investment is financially viable.It presents favorable business prospects, as indicated by the positive returns exceeding the minimum threshold in the NPV, IRR, and BCR analyses.Therefore, implementing UMPF as a venture can be continued with careful cost estimates.

Software Cost Estimation Model
The SCEM analysis in this research employs the Function Point method.SCEM analysis is conducted on the SmartAgri.idcloud server and the CFP value is calculated.The CFP value involves evaluating the function value for each complexity weight and multiplying it by its corresponding complexity factor.The result of SCEM applying Function Point Method can be seen in Figure 4.
Based on Figure 4, the quantity of each component that has been classified is then multiplied by the weight factor of each complexity level.The CFP value represents the total value of overall software complexity for the UMPF device.The CFP value affects the size, or the value used to estimate software development costs.If the CFP value increases, indicating a larger software size, it leads to higher estimates for software development costs.Therefore, after obtaining the CFP value, the calculation continues with the calculation of the RCAF for the software.The RCAF calculation is used to assess the complex conclusion of a software system based on 14 General System Characteristics (GSC) [15].The RCAF value obtained for the development of temperature and humidity monitoring software on UMPF is 56.Then, the lamp control software on UMPF is 55.71.
The RCAF value for the UMPF software has a relatively high complexity weight for temperature and humidity monitoring and lamp control.The elevated complexity weight assigned to temperature and humidity monitoring and lamp control results from the evaluation of 14 factors by programmers and developers, which exert a substantial and influential impact on the assessed software.The greater the number of lines of code produced, the higher the programming complexity.The KLOC value obtained in this study for temperature and humidity monitoring is 8266.72 lines of code, while lamp control on UMPF is 8247.20.In this research, the final effort value for temperature and humidity monitoring is 656.089, while the final effort value for controlling UMPF's lighting is 654.540.The work value is determined by allocating the final effort value to various activities.
The acquired final effort value is subsequently allocated among the 12 activities conducted in software development, with each activity having its proportionate share of tasks in the software development process.The complexity weight of software development or implementation on the device influences the SCEM analysis using FPA method.The total estimated cost for the development of UMPF software is Rp 16,545,742 for temperature and humidity monitoring and Rp 16,506,673 for controlling lighting, which is a relatively high estimated cost compared to the previous research by Nugroho et al. [11], where the development of AWLMS software resulted in a total estimated cost of Rp 4,630,476.The significant total estimated cost can be attributed to the complexity of the software development and the customization required to meet the needs of plant cultivation in UMPF.
The SCEM analysis systematically evaluates the anticipated workload in software development through the rigorous application of the Function Point Method.It meticulously delineates the sizes and complexities of diverse modules, providing precise insights.The resultant software cost estimates play a pivotal role in strategic decision-making, offering indispensable information for meticulous budgeting, strategic planning, and a robust assessment of the feasibility of implementing UMPF Technology.Undertaking a thorough economic analysis of Mini Plant Factory technology is imperative for guaranteeing not only its feasibility but also its long-term sustainability on a global scale.This all-encompassing analysis meticulously considers both software and hardware costs, meticulously weighing potential benefits such as heightened crop yields, diminished environmental impact, and bolstered food security.Furthermore, this economic analysis serves as a compass, guiding the identification of the most economically efficient implementation strategies, thereby ensuring the technology's sustainable deployment, and fortifying its enduring viability on the international stage.

Conclusion
The Urban Mini Plant Factory technology holds the potential to advance precision agriculture, fostering sustainable and efficient food resilience.This research confirms the economic justification for investing in UMPF technology.The careful planning and implementation of UMPF deployment aims to provide economic and operational benefits to both farmers and consumers, along with improvements in crop yields and reduced environmental impact.Based on this study, the UMPF investment is projected to yield positive returns after four years, surpassing the minimum threshold.The SCEM analysis reveals that the capabilities of programmers/developers significantly influence the software development workload, but the overall workload can be effectively managed and planned within budget constraints.This research establishes a robust foundation for UMPF implementation, showcasing its potential for economic and financial benefits.In future studies, our focus will be to initially prioritize developing more complex software, reducing overall development time, and ensuring critical features in UMPF are available as intended.

Figure 1 .
Figure 1.Research flow diagram for feasibility analysis of urban mini plant factory

Figure 2 .
Figure 2. Urban Mini Plant Factory (UMPF) factor.This research assumes an hourly wage rate for each software development activity based on the Decree of the Regent of Sleman Number 47.2/Kep.KDH/2021 on the Standard Unit Price of Goods and Services in Sleman Regency for the Fiscal Year 2022.

Figure 3 .
Figure 3. Result of benefit analysis for UMPF implementation

Figure 4 .
Figure 4. Result of Software Cost Estimation Model using the Function Point Method the context of function points, where FP represents the function point value, CFP denotes the total crude function point value, and RCAF signifies the total RCAF value.The constants 0.65, 0.01, and 8.2 are standardized values set by the Internal Function Point Users Group (IFPUG), while 56

Table 2 .
Assumptions and parameters in the field of engineering economics.