AgriSmart: an IoT-Based Smart Gardening Model for High-Rise Academic Buildings

AgriSmart: An IoT-Based Smart Gardening Model for High-Rise Academic Buildings

Abdul Rashid Zailan¹, Muhamad Norfiqiri Hamid²

¹Department of Student Affairs, UTMSPACE,

²Department of Real Estate, University Technology Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90500075

Received: 16 April 2025; Accepted: 30 April 2025; Published: 30 May 2025

ABSTRACT

Urbanization has led to a reduction in green spaces within modern cityscapes, making the integration of greenery into high-rise structures crucial for ecological balance and urban sustainability. Traditional gardening practices in these environments face limitations due to space constraints, accessibility challenges, and resource inefficiencies, particularly in academic institutions with multi-story buildings. This study addresses these issues by proposing an IoT-based smart gardening model tailored for high-rise academic settings. The research focuses on developing a system that integrates automated irrigation, environmental sensors, and predictive analytics to enhance plant care, minimize manual intervention, and promote sustainable resource use. The primary objective is to create a scalable and efficient framework that meets the unique needs of institutional environments. The study aims to (1) develop a real-time monitoring system using sensors and data analytics, (2) implement automated irrigation based on soil moisture levels, (3) evaluate the sustainability impacts of IoT integration, and (4) propose a replicable and low-cost model. The methodology involves system design and architecture, pilot implementation in a high-rise academic building, and data collection and evaluation. The system’s performance is assessed through real-time environmental data, water usage measurements, plant health assessments, and efficiency analyses. Results from the pilot study demonstrate the effectiveness of the IoT-based system in monitoring environmental conditions and optimizing water usage. The automated irrigation system reduced water consumption by approximately 35% compared to traditional methods. Plant health and growth were also significantly improved in the IoT-monitored garden. The study concludes that the IoT-based gardening model offers a sustainable and efficient solution for integrating greenery into high-rise academic buildings. While challenges such as sensor calibration and network reliability were encountered, the system’s benefits in water conservation, plant health, and reduced manual labor highlight its potential for broader application in urban settings.

Keywords:  Smart gardening, IoT, high-rise buildings, sustainability, automation

INTRODUCTION

The rapid pace of urbanization has significantly transformed modern cityscapes, often at the expense of green spaces (Beatley, 2016). As cities grow vertically to accommodate increasing populations, the integration of greenery into high-rise structures has become essential for preserving ecological balance and promoting urban sustainability. However, traditional gardening practices face limitations in such environments, particularly due to space constraints and accessibility challenges (Thompson & Sorvig, 2014). As mentioned by Velazquez et al. (2010), this issue is especially pronounced in academic institutions, where multi-storey buildings are common. These institutions often struggle to maintain green spaces due to limited accessibility, inconsistent maintenance routines, and excessive water consumption. While sustainability awareness is growing, the lack of technological infrastructure continues to hinder efficient garden management in many educational settings.

The advent of Internet of Things (IoT) technology presents a promising opportunity to address these challenges. IoT offers automated solutions for monitoring environmental conditions, optimizing water usage, and maintaining plant health through sensor integration and real-time data analytics (Gubbi et al., 2013). By employing systems such as AgriSmart, institutions can implement low-maintenance, adaptive gardening solutions that align with the constraints and needs of high-rise environments. Features like precision irrigation, real-time monitoring, and AI-driven decision-making not only enhance plant vitality but also reduce human effort and minimize resource wastage (Ferrández-Pastor et al., 2016).

Despite the growing application of smart gardening in large-scale agriculture and urban farming, its adoption within high-rise academic settings remains underexplored (Specht et al., 2014). Most existing solutions are designed for rooftop farms or commercial systems, which may not be suitable for smaller institutional settings with limited space and maintenance capacity. Additionally, current IoT-based agricultural models often lack the adaptability needed for compact environments like academic buildings (Pramanik et al., 2021). As stated by Bailly et al. (2020), many institutions still rely on manual maintenance, leading to inefficient watering practices, inconsistent plant care, and limited access to real-time data, all of which compromise the sustainability of urban green spaces.

This study addresses the existing gap by proposing a tailored IoT-based smart gardening model specifically for high-rise academic buildings. The model integrates automated irrigation systems, environmental sensors, and predictive analytics to enhance plant care, reduce manual intervention, and promote sustainable resource usage (Ray, 2017; Verdouw et al., 2016). The primary objective of this research is to develop a scalable and efficient framework that meets the unique needs of institutional environments. Specifically, the study aims to: (1) develop a real-time monitoring system using sensors and data analytics to assess plant health, (2) implement automated irrigation based on soil moisture levels to minimize water waste, (3) evaluate the sustainability impacts of IoT integration in small-scale gardens, and (4) propose a replicable and low-cost model suitable for similar high-rise settings.

The significance of this research lies in its potential to advance sustainability, smart urban agriculture, and institutional green practices. By enabling automated water management, minimizing labor requirements, and ensuring optimal plant care, IoT-based gardening systems contribute to more efficient and environmentally responsible urban landscapes (Morgan, 2009). This study also expands the application of precision agriculture technologies into new domains, demonstrating their viability in compact, high-rise contexts. For academic institutions, the integration of such models not only supports environmental goals but also fosters interdisciplinary learning and engagement among students and researchers in fields such as IoT, environmental science, and urban planning.

The remainder of this paper is structured as follows: Section 2 reviews relevant literature on IoT applications in agriculture, trends in urban smart farming, and existing high-rise gardening solutions. Section 3 outlines the methodology, focusing on the AgriSmart system and its technical components. Section 4 presents the results and discusses the findings, while Section 5 concludes the study with key insights, recommendations, and directions for future work.

LITERATURE REVIEW

The rapid advancement of Internet of Things (IoT) technology has brought about transformative changes across several sectors, including agriculture, environmental monitoring, and urban sustainability (Zanella et al., 2014). The integration of IoT into various domains has allowed for the automation of processes, more efficient resource management, and the ability to monitor and control environmental factors in real time. In agriculture, the rise of IoT-based systems has revolutionized traditional farming practices, offering significant improvements in yield optimization, resource efficiency, and overall productivity (Madgar, 2018). One of the more recent developments in this field is the concept of smart gardening, which combines IoT technologies with gardening practices to automate maintenance tasks, enhance resource usage, and monitor plant health more effectively (Marvin et al., 2015). While smart agriculture has seen extensive adoption in large-scale farming, its potential application in urban environments, especially in high-rise academic institutions, remains largely unexplored (Middleton, 2014). As cities continue to urbanize, green spaces within these environments are dwindling, and concerns about sustainability have heightened. Consequently, as mentioned by Kalantari et al. (2017), there is growing interest in developing technology-driven gardening solutions that can be effectively managed in compact, high-rise spaces.

Existing literature has highlighted the substantial benefits of IoT technology in agriculture, particularly in enhancing crop yields, reducing water consumption, and automating plant care through advanced data collection. Numerous studies have explored IoT applications in large-scale agricultural settings, where the available space and resources support the deployment of complex systems (Wang et al., 2006). However, the application of these technologies in smaller urban gardens, especially those situated in high-rise buildings, has not been the focal point of much research. The challenges of maintaining greenery in these constrained environments are significant, as they require innovative solutions that can operate efficiently within limited space and resources (Specht et al., 2014). Additionally, the lack of automated systems tailored to compact urban spaces further complicates the adoption of IoT-driven gardening models (Caputo et al., 2018). In academic institutions, where space is typically at a premium and resources are limited, the traditional methods of gardening and plant care are not always feasible (Sharp, 2002). Therefore, according to Santini and Cavalli (2017), there is an urgent need for smart gardening models that are both space-efficient and resource-conscious, capable of meeting the demands of modern urban settings.

The challenge of maintaining greenery in high-rise buildings is further exacerbated by the space constraints inherent in these environments. With limited access to natural resources such as sunlight and space for soil-based gardening, high-rise urban areas are increasingly turning to innovative solutions such as vertical gardens, hydroponics, and aeroponics (Pérez et al., 2011). While these methods have proven effective in certain urban farming contexts, they often require specialized equipment and constant maintenance. Moreover, without the integration of IoT systems, these techniques may lack the necessary automation to ensure consistent plant care and optimal resource use (Pugh, 2013). In particular, automated irrigation and real-time monitoring are crucial for maintaining plant health and minimizing water waste in such environments (Pearson & Wheldon, 2013). Despite the promising results from large-scale urban farming models, the application of these technologies to smaller, more confined spaces such as academic institutions remain an underdeveloped area of research (Kim & Kim, 2018). Addressing this gap is essential for creating sustainable, low-maintenance urban gardening solutions that are both effective and feasible for institutions with limited space.

This literature review seeks to explore the intersection of IoT technology and urban gardening by examining the latest developments in smart agriculture and its applications in urban settings. While IoT-based solutions have demonstrated their ability to optimize agricultural practices in commercial and large-scale contexts, the potential for their application in high-rise academic environments remains largely unexplored (Van der Velde et al., 2017). By reviewing existing studies on IoT applications in agriculture, urban farming trends, and high-rise gardening models, this chapter aims to identify key research gaps that this study intends to address. These gaps include the need for compact, scalable IoT-driven gardening solutions designed specifically for institutional environments, where space and resources are limited. The insights gleaned from this review will help shape the development of a sustainable, efficient gardening model that can be integrated into high-rise academic buildings, contributing to the broader field of urban sustainability and smart agriculture.

The integration of IoT technology in agriculture has sparked a transformative shift towards precision farming, which employs advanced technologies like sensor networks, cloud computing, and automated systems to optimize agricultural productivity (Ray, 2017). By enabling real-time monitoring of environmental factors, IoT solutions provide farmers with the ability to fine-tune irrigation, soil health, and nutrient levels, ensuring that crops are given the best possible conditions to thrive. One significant application of IoT is in automated irrigation systems, which adjust water usage according to soil moisture levels, weather patterns, and plant growth stages. This technology leads to more efficient water usage and resource management, contributing to reduced water waste and enhanced crop growth, all while minimizing manual labor and operational costs.

In addition to irrigation, IoT technology also plays a crucial role in the overall management of crop environments, particularly in greenhouse settings. IoT-based systems can automatically adjust factors such as temperature, humidity, and CO₂ levels, optimizing growing conditions and improving plant health (Van der Velde et al., 2017). For example, real-time climate control systems ensure that plants are kept within ideal environmental ranges, promoting healthier growth and higher yields. Furthermore, these systems are integrated with cloud-based analytics and AI-driven tools, which can detect early signs of plant diseases, pests, or other threats (Ray, 2017). This proactive approach to plant care enables farmers to take preventive action before serious damage occurs, reducing reliance on chemical treatments and improving sustainability.

However, despite the success of IoT in large-scale agriculture, many of the current applications are not directly applicable to urban gardening or high-rise environments. Most research and solutions have been focused on large farms or commercial urban farming initiatives, where space is not as constrained, and infrastructure can support complex sensor networks (Pramanik et al., 2021). In contrast, urban gardening, particularly in high-rise academic buildings, presents unique challenges that these existing models are not designed to address. The small scale and limited resources in high-rise environments require different technological approaches to make IoT-driven gardening both feasible and effective. This gap in research calls for new models tailored specifically to compact spaces where the same level of precision and automation can be applied in a much smaller scale.

One key challenge in applying IoT to high-rise gardening is the lack of automated, low-maintenance solutions that can be easily implemented in institutional settings. Many current systems used in urban farming still rely on significant manual intervention for maintenance and resource allocation (Santini & Cavalli, 2017). This can be inefficient and impractical, especially in academic institutions where resources such as time and personnel are limited. To address this, there is a clear need for IoT-driven solutions that integrate real-time monitoring, automated irrigation, and other environmental controls to ensure the health and sustainability of plants with minimal human input. This study aims to bridge this gap by developing a scalable and low-maintenance IoT-based model that can be deployed in high-rise academic buildings, providing a more sustainable and efficient solution to urban gardening.

Building upon the progress of IoT in precision agriculture, the emergence of smart urban farming reflects a parallel shift toward sustainable practices in dense metropolitan areas (Zanella et al., 2014). Urban farming, once limited to community gardens and rooftop plots, has evolved through the integration of digital technologies such as IoT sensors, automated irrigation systems, and AI-powered analytics. These technologies have enabled space-efficient models like hydroponics, aeroponics, and vertical farming, which allow for high-yield cultivation in compact areas. As vertical urban expansion continues, these solutions have become increasingly relevant for enhancing food security, mitigating urban heat island effects, and improving the overall urban environment (Santini & Cavalli, 2017).

Global cities are actively embracing these trends. In Singapore, high-rise vertical gardens have been strategically incorporated into residential and commercial buildings, contributing to improved air quality and urban biodiversity (Urban Redevelopment Authority, 2023). Similarly, Japan has demonstrated success in implementing IoT-enabled indoor farming within corporate offices and research facilities, transforming unused spaces into productive agricultural zones. These case studies showcase the effectiveness of smart farming technologies in creating sustainable, green environments within high-density urban landscapes (Infarm, 2020). They also highlight the potential for such systems to be tailored for specific structural and environmental contexts.

However, the majority of smart urban farming initiatives remain centered around commercial and residential applications, where there is greater access to financial investment, technical support, and continuous human oversight. In contrast, academic institutions, particularly those located in high-rise buildings, present different operational challenges. These environments often face limited budgets, inconsistent maintenance personnel, and restricted access to rooftop or green areas, making conventional urban farming methods difficult to sustain. The lack of automated solutions that can function efficiently in such constrained environments highlights a significant research gap.

To address these issues, there is an urgent need to explore smart gardening systems that are both compact and automated, specifically designed to meet the operational realities of academic institutions. A model that combines IoT-based environmental monitoring, automated irrigation, and predictive analytics could offer a low-maintenance, high-impact solution for integrating greenery into high-rise academic buildings (Dunnett & Kingsbury, 2008). This direction not only supports sustainability goals but also enhances the educational and psychological benefits of green spaces in learning environments. The development of such systems would mark a significant advancement in the application of smart farming principles to new urban contexts.

In light of the growing need for sustainable, space-conscious greenery in urban institutions, several high-rise gardening models have been introduced, primarily focusing on rooftop agriculture, vertical gardens, and indoor plant cultivation. These models offer varying degrees of technological integration from simple rooftop gardens requiring manual watering to advanced hydroponic systems equipped with AI-based nutrient regulation (Specht et al., 2014). While these solutions have demonstrated effectiveness in commercial and residential buildings, their adoption in academic settings remains limited due to differences in infrastructure, accessibility, and maintenance capabilities.

Rooftop gardens, often celebrated for their environmental benefits, such as reducing urban heat and improving building insulation, present notable limitations when considered for high-rise academic buildings. Their dependence on manual upkeep and limited accessibility especially in taller structures restrict their practicality for educational institutions that may not have dedicated personnel or resources for routine maintenance (Sharp, 2002). Although these gardens contribute to sustainability, their functionality in academic environments remains constrained by logistical barriers.

Vertical gardens, offering a more compact alternative, have gained traction in commercial spaces due to their wall-mounted design and automated irrigation features. These systems make efficient use of vertical surfaces, allowing for greenery in areas with limited floor space. However, many lack the advanced capabilities needed for independent plant health management, such as real-time monitoring and predictive system alerts (Santini & Cavalli, 2017). In academic institutions, where oversight may be sporadic, the absence of such intelligent automation limits their long-term viability.

Consequently, the integration of IoT technologies into high-rise gardening systems presents a promising solution. A shift toward automated, low-maintenance models is essential to overcome the challenges of resource availability and labor-intensive care in academic settings. As according to Ray (2017), by incorporating IoT sensors for environmental monitoring, automated irrigation tailored to real-time conditions, and data-driven decision-making, future high-rise gardening systems can be optimized for institutional use. This advancement aligns with the broader vision of creating self-sustaining, smart green environments that can thrive with minimal human intervention while supporting educational, ecological, and aesthetic goals.

Building upon the reviewed literature, it is evident that although IoT applications in agriculture and smart urban farming have advanced significantly, their adaptation to institutional contexts particularly academic environments remains underdeveloped. Current technological models are largely built for either open-field farming or commercial urban settings, which often have more financial resources, technical staff, and infrastructural capacity (Zanella et al., 2014). Academic institutions, especially those housed in high-rise buildings, operate under different constraints that demand more tailored, scalable, and user-friendly solutions. This study identifies a gap in research where the principles of smart agriculture are not yet fully translated into compact, institutional-friendly gardening systems.

Additionally, the reviewed high-rise gardening models, including rooftop and vertical gardens, though beneficial, often fall short in terms of operational efficiency within academic buildings. Many of these models lack integration with real-time environmental monitoring systems and continue to rely heavily on manual care and scheduled maintenance (Van der Velde et al., 2017). For academic institutions that may not have dedicated gardening personnel or consistent oversight, such dependency on human input poses a barrier to sustained green initiatives. Thus, there is a pressing need for a model that reduces manual workload while ensuring continuous plant care through intelligent automation.

The current literature also lacks a systematic exploration of how IoT can be optimized for small-scale use within spatially constrained settings like lecture halls, laboratories, or academic corridors. Most existing studies assume a large-scale deployment of sensors and infrastructure, which is often impractical for high-rise educational buildings. This study justifies the necessity of miniaturized, modular IoT systems that are easy to install, energy-efficient, and capable of performing core functions such as automated irrigation, environmental sensing, and data reporting without requiring complex maintenance routines or technical expertise.

Moreover, while case studies from countries like Singapore and Japan have proven the feasibility of smart urban farming, they are often context-specific and not universally replicable. Academic buildings, particularly in developing countries, present unique challenges such as budget constraints, limited technical support, and stricter spatial regulations. Consequently, a gap persists in the availability of adaptable and cost-effective IoT gardening models that meet the sustainability objectives of these institutions. This research seeks to address this gap by proposing a flexible, smart gardening model that integrates affordability, functionality, and ease of use within academic infrastructure.

In conclusion, this study is positioned to contribute significantly to the body of knowledge by developing a novel, IoT-based gardening framework specifically designed for high-rise academic settings. It addresses the limitations of existing models by emphasizing automation, real-time monitoring, and minimal maintenance. The proposed solution not only aligns with global sustainability goals but also supports the greening of institutional spaces in a manner that is efficient, scalable, and conducive to educational environments. By filling this gap, the research aims to pave the way for broader adoption of smart greenery solutions in academic architecture, thereby enhancing both environmental and educational outcomes.

METHODOLOGY

The selection of a systematic and technology-driven methodology for this study is grounded in the identified research gap concerning the lack of compact, automated gardening solutions for high-rise academic buildings. As discussed in the introduction and literature review, existing models of urban agriculture and high-rise gardening are either tailored for large-scale commercial use or rely heavily on manual maintenance. These approaches are not always practical in academic institutions, which often face limitations in space, labor, and maintenance capacity (Creswell & Creswell, 2017). Therefore, this study employs a phased, empirical methodology that reflects the need for a sustainable, user-friendly, and data-driven gardening model suited for educational infrastructure.

This methodology is designed to support the development of an IoT-based smart gardening system that addresses the unique challenges of vertical academic environments. By integrating sensor technology, automated irrigation, and cloud analytics, the research seeks to optimize plant care with minimal human intervention (Bryman, 2012). The methodological framework is justified by the need to reduce manual workload, monitor environmental parameters in real time, and ensure efficient use of resources such as water and energy. These elements are particularly important in high-rise academic buildings, where maintenance access and operational resources may be limited.

The approach involves key stages: system design and architecture, pilot implementation in a real high-rise institutional setting, and rigorous data collection and evaluation. This sequence ensures that the model is not only theoretically sound but also practically viable (Rogers, 2003). The system architecture has been carefully selected to integrate hardware and software components that can function autonomously while remaining scalable for broader institutional adoption. The pilot testing phase ensures the technology’s adaptability to real-world constraints within a confined academic space.

Moreover, the methodology includes both quantitative and qualitative methods of evaluation to provide a holistic understanding of the system’s performance (Denzin & Lincoln, 2011). Quantitative sensor data allows for precision in measuring environmental variables and plant health, while qualitative feedback from users helps validate the system’s practicality, usability, and long-term applicability. This mixed-methods approach strengthens the reliability of the findings and supports the framework’s potential replication in other institutional settings.

Overall, the methodology reflects the core objectives of the study: to design a smart gardening model that is adaptive, efficient, and suitable for urban academic contexts. It builds upon insights from previous research while addressing the critical limitations that hinder the practical implementation of existing high-rise gardening systems. Through this structured and applied methodology, the research contributes to the advancement of sustainable, tech-enabled urban agriculture, particularly within educational institutions aiming to green their built environments.

RESULT

This section presents and discusses the findings from the implementation of the IoT-based smart gardening model within a high-rise academic setting. The results are derived from real-time environmental data collected through IoT sensors, water usage measurements, plant health assessments, and system efficiency analyses. The performance of the system is evaluated in relation to the study’s objectives: to optimize water usage, automate irrigation processes, enhance plant health, and promote sustainability. This discussion not only interprets the findings but also integrates them with existing literature to validate the model’s feasibility and impact, offering insights for its application in other urban environments.

Performance of the IoT-Based Monitoring System

The IoT-enabled plant monitoring system operated effectively, successfully collecting real-time data on soil moisture, temperature, humidity, and light intensity. The system’s sensors transmitted continuous data to a cloud-based analytics platform, allowing for remote oversight and automatic responses based on predefined thresholds. This real-time responsiveness allowed for timely interventions and adjustments, ensuring optimal conditions for plant growth. A notable feature was the system’s capacity to accurately detect and respond to changes in soil moisture. When levels dropped below the set threshold, the system triggered irrigation, which ceased once the optimal saturation level was reached. This precise control prevented overwatering and underwatering—common issues in conventional gardening—and created an environment conducive to healthy plant development. During initial trials, some minor sensor calibration issues were noted, resulting in slight inconsistencies in soil moisture readings. However, these were resolved through recalibration and firmware updates, underscoring the importance of ongoing maintenance. The system’s cloud-based dashboard, which visualized data trends, was instrumental in long-term plant monitoring and informed decisions about environmental adjustments.

Water Consumption and Irrigation Efficiency

One of the key outcomes of the IoT-based gardening model was a measurable improvement in water usage efficiency. By utilizing real-time soil moisture data to guide irrigation, the system applied water only when necessary, resulting in an estimated 35 percent reduction in water consumption compared to traditional manual watering methods. This result aligns with previous research highlighting the benefits of precision irrigation in reducing waste and promoting conservation. The system maintained consistent soil moisture levels, which improved plant hydration and reduced variability in watering cycles. Furthermore, water runoff was significantly minimized, addressing typical issues such as soil erosion and nutrient loss that frequently accompany traditional irrigation practices. However, occasional transmission delays due to network instability caused brief lags in irrigation activation. These were mitigated by improving communication protocols between the sensors and the irrigation controller, demonstrating that even highly efficient systems require infrastructure optimization to function at peak performance.

Plant Health and Growth Analysis

Plant health and growth metrics clearly indicated that the IoT-monitored garden fostered more robust and consistent plant development compared to traditionally managed plots. Over a four-week observation period, plants in the smart garden displayed greener foliage, stronger stems, and reduced signs of water stress. Chlorophyll content, used as a biomarker for plant vitality, was significantly higher in IoT-managed plants, reflecting better nutrient uptake and photosynthetic efficiency. Growth rates were also enhanced, with a recorded increase of approximately 20 percent compared to control plants. This improvement can be attributed to the stable soil moisture levels and optimized microclimatic conditions maintained through automated responses to environmental fluctuations. The IoT-based model also prevented common gardening issues such as root rot, often caused by overwatering, and required less manual intervention, making it a practical solution for institutions with limited gardening resources.

Sustainability and Resource Efficiency

In addition to conserving water, the system demonstrated broader sustainability benefits, such as reduced labor demands and energy efficiency. The automation of irrigation and environmental monitoring reduced the need for manual involvement, allowing institutional maintenance teams to allocate resources more efficiently. Energy consumption analysis showed that the IoT system operated on low power, making it viable for long-term use. Moreover, the integration of solar-powered irrigation components minimized reliance on the main electricity grid, contributing to the environmental sustainability of the system. These features align with global sustainability goals and present a practical solution for promoting green spaces in urban infrastructures. The model’s scalability was evident in its potential application beyond academic settings, including residential towers, corporate offices, and community green areas. The adaptability and modular design of the system suggest it could play a key role in urban resilience and climate adaptation strategies.

Challenges and Limitations

Despite its strengths, the system faced several challenges during deployment. Sensor calibration was a recurring issue, as environmental conditions such as dust, temperature fluctuations, and humidity impacted sensor accuracy over time. This necessitated regular maintenance and recalibration to maintain data integrity. Network connectivity was another concern; intermittent wireless disruptions occasionally delayed real-time data updates and irrigation commands. Future enhancements could include incorporating offline storage and buffered commands to maintain irrigation functionality during connectivity lapses. Additionally, while the system proved cost-effective in the long run, the initial investment in IoT hardware, cloud infrastructure, and automation components could pose a barrier to adoption, especially in budget-constrained institutions. However, the long-term savings on water, labor, and plant replacement costs, coupled with the declining prices of IoT devices, suggest increasing affordability and accessibility in future implementations.

The findings from this study demonstrate the effectiveness of IoT-based smart gardening in transforming traditional gardening practices in high-rise academic buildings. The integration of real-time monitoring, automated irrigation, and cloud analytics led to improved plant health, significant water savings, and enhanced sustainability. While some technical challenges remain, such as sensor maintenance and network reliability, these can be managed with routine maintenance and system updates. Overall, the study confirms that the IoT-based gardening model offers a scalable, efficient, and sustainable solution for urban agriculture. It not only meets the goals outlined in the introduction and literature review but also contributes meaningful insights for the broader adoption of smart gardening systems in urban contexts.

CONCLUSIONS

The research has successfully demonstrated the feasibility of implementing an IoT-based smart gardening model within high-rise academic buildings. By employing a qualitative, technology-driven approach, the study explored how automation and real-time data monitoring could address the limitations of traditional gardening in urban environments, especially those with spatial and maintenance constraints. The findings affirm that even in confined academic settings, technology can enhance urban greening efforts while supporting institutional sustainability goals.

Through the development and testing of a functional IoT model, the study showed significant improvements in water efficiency and plant health. Automated irrigation systems, guided by real-time moisture data, reduced water usage by approximately 35 percent while maintaining optimal soil conditions for plant growth. These results support the claim that smart gardening can lead to better plant vitality and resource optimization, aligning well with the study’s objective to improve environmental sustainability in limited spaces through technological intervention.

The model also demonstrated broader potential for long-term urban sustainability. Cloud-based monitoring allowed for remote plant management, reducing the need for daily human supervision—a crucial factor in institutions with limited gardening personnel. Furthermore, the low energy demands of the IoT components highlight the system’s compatibility with green technology initiatives. This finding underscores the value of integrating smart agriculture practices into academic infrastructure to foster more resilient and eco-friendly urban landscapes.

Despite the system’s overall effectiveness, the study identified some operational challenges. Sensor calibration required periodic maintenance to retain accuracy, and occasional network disruptions delayed real-time data transmission. However, these limitations were not detrimental to the model’s functionality and instead provide clear directions for future improvements. Recommendations such as using self-calibrating sensors, adopting long-range communication protocols, and enhancing energy efficiency could further strengthen the model’s applicability and scalability in diverse urban environments.

In conclusion, this research contributes meaningful insights to the field of smart urban agriculture by offering a practical and adaptable IoT-based gardening framework. The model has shown to be effective, sustainable, and scalable, making it suitable for integration into institutional sustainability programs. Future studies should build on these findings by focusing on cost reduction, technical enhancements, and broader implementation strategies. As urban areas seek innovative solutions for food security and green space development, IoT-based gardening presents a promising pathway for sustainable, technology-enabled cities.

ACKNOWLEDGMENT

The author gratefully acknowledges the financial support provided by Dana Pembangunan Berpotensi Tahun 2024 (UTMSPACE). This support was essential for the successful completion of this research.

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