# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 1 Introduction
### Artificial Intelligence in Hydroponic Systems  
Recent research has explored different computational methods to improve nutrient management in hydroponic systems. By optimizing the nutrient supply, the system will increase both the growth period and overall development of plants. In hydroponics, the absence of soil makes plants more vulnerable to environmental fluctuations and variations in nutrient levels [18]. As a result, accurately dosing nutrients faces challenges. Traditionally, nutrient mixing in these systems measures electrical conductivity (EC) and pH levels. This approach generates imbalances at the nutrient level, which compromises the hydroponic culture. For example, a study presented in work [19] highlighted that conventional systems cannot adequately address imbalances caused by the lack or excess of specific ions. This results in suboptimal crop growth. To solve this problem, the integration of machine learning (ML) algorithms is recommended [20; 21]. The ML algorithms are trained using large datasets to determine nutrient amounts to make appropriate adjustments [22]. These infrastructures use software components and hardware elements, including modern sensors and actuation components [23].  
The absence of soil directly results in plants' heightened sensitivity to the amount of nutrients supplied. Therefore, the nutrient delivery system must be precise and operate continuously within optimal parameters. Based on these considerations, [PERSON] et al. [24] emphasize the importance of advanced monitoring systems that detect real-time anomalies in environmental or nutrient conditions. In general, AI solutions can integrate predictive components that can anticipate potential causes of anomalies within the system. Furthermore, automatic systems have corrective action components to prevent situations that could compromise the hydroponic culture [25]. To develop such systems, the monitored environmental variables (such as temperature, humidity, and lighting conditions) that can influence nutrient uptake must be identified beforehand [26].  
In addition to the monitoring component, automatic systems equipped with AI require specific calibration procedures tailored to the type of hydroculture. Depending on the crop type, the analyzed parameters vary, implying different configurations at nutrient and environmental parameter adjustment levels [4]. [PERSON] et al. [27] explore the possibility of using deep learning (DL) models to optimize nutrient mixtures based on crop type. The study highlights that DL models require large datasets, which is a disadvantage in practice due to the high variability of environmental conditions [28]. A direct consequence is reflected in the economic implications of adopting AI technologies for hydroponic solutions. Although AI tools can reduce labor costs and improve yields, the initial investment in technology and infrastructure can be prohibitive for many small-scale farmers [29; 30]. Economic analyses indicate that the benefits of AI integration must be evaluated in terms of the costs of developing hardware and software [31; 32]. In addition to the initial hardware and software development costs, the economic feasibility analysis of using AI in hydroponics must also include long-term operational costs. Cloud-based AI solutions involve monthly subscriptions. For example, for the Microsoft Azure cloud platform, the calculator used to estimate long-term costs can be consulted at the address [33]. Local systems may have lower costs, but they require constant maintenance.