Azuatalam Reginald Gbenga

Increasing global temperatures and unpredictable weather threaten agricultural production, intensifying the reliance on greenhouse cultivation. While naturally ventilated greenhouses offer an energy-efficient solution, their thermal performance under high heat loads is a growing concern. This study developed and validated a 3D CFD model to investigate the temperature distribution inside a single-span, naturally ventilated greenhouse. The model, implemented in ANSYS Fluent, considered the influence of external meteorological conditions, including solar radiation and wind, on the internal microclimate. The model demonstrated high accuracy, with an RMSE of 0.847°C and an MAE of 0.714°C for temperature when compared to experimental data. Diurnal analysis confirmed the dominant role of solar radiation, showing a strong correlation between indoor temperature and external solar radiation (r = 0.96) on sunny days. Spatially, the simulation revealed extreme thermal heterogeneity under high solar load, with a total internal temperature variation of up to 25°C (from 296 K to 321 K). The distribution was characterized by a distinct vertical stratification due to buoyancy and a strong longitudinal gradient, with the hottest air accumulating in the upper leeward corner. These findings highlight areas of potential crop heat stress and demonstrate the limitations of side-ventilation alone. The validated model serves as a valuable tool for optimizing ventilation strategies and improving climate control system design.

Vertical farming or vertical growing systems, a subset of controlled environment agriculture (CEA), integrates smart and precision agriculture to optimize crop production in stacked environments within a greenhouse, utilizing Internet of Things (IoT) sensors, automation, and advanced lighting. However, vertical farming can be carried out in the open, but the advantages of a closed system are sacrificed. This review examines recent advancements, including precision irrigation (hydroponics, aeroponics, and aquaponics), LED spectral optimization, robotics, and machine learning (ML) for enhanced resource efficiency. Case studies from Singapore, Japan, and the Netherlands demonstrate yield increases of 200-300% and energy savings of 30-50% compared to traditional methods. By integrating robotics, ML, and IoT, yields can be maximized, resource utilization can be optimized, and environmental impact can be reduced. Case studies from Singapore, Japan, and the Netherlands demonstrate the potential, with kale yields of 300 t•ha-1 , strawberry production at 200 t•ha-1 , and dual basil fish outputs achieved through 98% water recycling. Challenges such as high costs, energy demands, and technical complexity persist, particularly in developing regions. Future directions propose the integration of renewable energy, can offset energy reliance, cutting costs by 50%, low-cost sensors that broaden access and simplify IoT deployment, and modular designs that enhance scalability and offer hope for adaptation. This manuscript synthesizes quantitative metrics, comparative analyses, and implementation challenges, providing engineers and researchers with a detailed framework to advance sustainable urban agriculture through technology-driven solutions.