SMART MANAGEMENT OF VERTICAL IRRIGATION SYSTEMS USING AI-POWERED INTERNET OF THINGS (IOT) DEVICES

Аннотация

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has revolutionized agricultural practices, particularly in vertical irrigation systems. This paper explores the application of AI-powered IoT devices to optimize water usage, enhance crop yield, and reduce operational costs in vertical farming. By leveraging real-time data analytics, machine learning algorithms, and automated control systems, these technologies enable precise irrigation management. The study presents a methodology for implementing such systems, analyzes their performance through empirical data, and discusses their implications for sustainable agriculture. Results indicate significant improvements in water efficiency and crop productivity, with potential scalability across diverse farming environments. Keywords: Artificial Intelligence, Internet of Things, Vertical Irrigation, Smart Agriculture, Water Efficiency, Machine Learning. I. INTRODUCTION Vertical farming has emerged as a sustainable solution to address food security challenges in urban areas, where land availability is limited. However, efficient water management remains a critical issue due to the high water demands of vertical irrigation systems. Traditional irrigation methods often lead to water wastage and https://scientific-jl.org/obr Выпуск журнала №-72 317 Часть–1_ июня–2025 ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ 2181- 3187 inconsistent crop growth. The advent of AI and IoT technologies offers a promising approach to address these challenges by enabling data-driven decision-making and automation [1]. Recent literature highlights the transformative potential of IoT in agriculture. For instance, studies have shown that IoT sensors can monitor soil moisture, temperature, and humidity in real time, enabling precise irrigation scheduling [2]. Meanwhile, AI algorithms, particularly machine learning models, can analyze vast datasets to predict optimal watering needs and detect anomalies in irrigation systems [3]. The synergy of AI and IoT allows for the development of smart irrigation systems that adapt to environmental changes and crop requirements dynamically [4]. Despite these advancements, gaps remain in the practical implementation of AI- powered IoT systems in vertical farming. Many studies focus on horizontal farming or greenhouse setups, with limited attention to vertical irrigation systems [5]. Furthermore, the integration of predictive analytics and automated control mechanisms in resource-constrained environments requires further exploration [6]. This paper aims to fill these gaps by proposing a framework for smart management of vertical irrigation systems using AI-powered IoT devices, supported by empirical data and analytical tools. II. METHODOLOGY Research Approach. This study adopts a mixed-methods approach, combining experimental design with data analysis to evaluate the performance of AI-powered IoT devices in vertical irrigation systems. The research was conducted in a controlled vertical farming setup, simulating urban agricultural conditions. System Design. The proposed system integrates IoT sensors (e.g., soil moisture sensors, temperature sensors, and flow meters) with an AI-driven control unit. The IoT devices collect real-time data on environmental parameters, which are transmitted to a cloud-based platform. Machine learning models, specifically Random Forest and Neural Networks, analyze the data to predict irrigation needs and optimize water distribution. The system architecture is depicted in Figure 1. https://scientific-jl.org/obr Выпуск журнала №-72 318 Часть–1_ июня–2025 ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ 2181- 3187 Figure 1: System Architecture of AI-Powered IoT Irrigation System Diagram illustrating the flow of data from IoT sensors to the AI control unit, cloud platform, and automated irrigation actuators. The diagram shows sensors (soil moisture, temperature, flow meters) connected to a Raspberry Pi, which interfaces with a cloud server running machine learning models. Arrows indicate data flow to actuators controlling water distribution. Data Collection. Data were collected over a six-month period from a vertical farming setup with 100 planting units. Parameters included soil moisture levels, water usage, crop growth rates, and energy consumption. The dataset comprised 10,000 data points, sampled at 15-minute intervals. https://scientific-jl.org/obr Выпуск журнала №-72 319 Часть–1_ июня–2025 ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ 2181- 3187 Analytical Methods. The performance of the system was evaluated using the following metrics: Water use efficiency (WUE): Calculated as the ratio of crop yield to water consumed (kg/L). Crop yield: Measured as the total biomass produced per planting unit (kg/m²). Energy efficiency: Assessed as the energy consumed per unit of water delivered (kWh/L). A comparative analysis was conducted between the AI-powered IoT system and a traditional timer-based ir