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

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.