Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture.

In this study, an ion monitoring system using ion-selective electrodes (ISEs) was developed to maintain nutrient solutions in closed-loop hydroponic systems within plant factories. Based on this monitoring system, an algorithm and individual ion supplementation system were developed to calculate the optimal amounts of ion replenishment for crop growth. The developed algorithms adopt distinct cost functions to achieve their respective goals. The ion balance-prioritized algorithm minimizes the cost function, which is defined as the sum of the squared differences between the target ion concentrations and the calculated ion concentrations. In contrast, the carbon emission reduction algorithm minimizes the cost function defined as the total carbon emissions, which are calculated by multiplying the individual salt supplementation amounts by their respective carbon emission coefficients. Simulation results demonstrated that the ion balance-prioritized algorithm achieved average errors of 5.5 ± 5.5%, 0.4 ± 0.5%, and 5.0 ± 11.2% for Ca, NO₃, and K, respectively, showing superior performance in maintaining ion balance. Meanwhile, the carbon emission reduction algorithm achieved a total of 0.064 kg CO₂, representing a 50.19% reduction compared to the ion balance-prioritized type. The developed algorithms and system are expected to reduce costs by minimizing nutrient and water usage through the recycling of waste nutrient solutions. Furthermore, they are anticipated to improve crop productivity by maintaining an optimal ion composition and to contribute to sustainable agriculture through carbon emission reduction.

Continued population growth demands a significant increase in agricultural production to ensure food security. However, agricultural output is limited by environmental crises and the negative impacts of open-field farm practices. As an alternative, vertical farming techniques, such as aeroponics, can be utilized to optimize the use of resources. However, the uneven size and distribution of spray droplets in aeroponics, issues that affect root development and nutrient delivery, continue to be problematic in spray performance analysis. In aeroponics, nutrient solutions are delivered to plant roots through pressurized nozzles, and the effectiveness of this delivery depends on the spray characteristics. Variations in flow rates directly affect droplet size, density, and coverage, which in turn influence nutrient uptake and crop growth. In this study, the flow rate was adjusted (3, 4.5, and 6 L/min) to quantitatively analyze spray performance using water-sensitive paper (WSP) as a deposit collector via a quick assessment method. Subsequently, image-processing techniques such as threshold segmentation and morphological operations were applied to isolate individual spray droplets on the WSP images. This technique enabled the quantification of the droplet’s coverage area, size, density, and uniformity to effectively evaluate spray performance. One-way ANOVA indicated that all the spray parameters varied significantly with respect to the flow rate (p < 0.05): For example, the average diameters of the droplets increased from 0.73 mm at 3 L/min to 1.29 mm at 6 L/min. The droplets’ densities decreased from 85.53 drops/cm² to 30.00 drops/cm² across the same flow range. The average uniformity index improved from 30.53 to 15.95 as the flow rate increased. These results indicate that the application of WSP is an effective and scalable approach for analyzing spray performance in aeroponics, as WSP can be rapidly digitized with simple tools, such as a cell phone camera, avoiding the limitations of flatbed scanners or specialized imaging systems.