AI-Driven Robotic Systems for Efficient Hydroponic Water Circulation and Purification Management

摘要

The development of AI-driven robotic systems offers substantial prospects for improving hydroponic farming methods, especially in water circulation and purification oversight. This work investigates using Reinforcement Learning (RL) algorithms in robotic systems to enhance fertilizer delivery and water quality in hydroponic settings. By persistently monitoring essential characteristics like pH, turbidity, and nutrient content, the robotic systems may implement real-time modifications to the water circulation process. The RL algorithm facilitates the system's learning from environmental input, enhancing decision-making over time and providing optimum plant growth conditions. Simulation and field testing demonstrated that the deployment of AI-driven robots results in less water waste, improved fertilizer efficiency, and greater plant health. Moreover, the automation of repetitive chores reduces labor expenses and operational intricacies, facilitating sustainable agriculture methods. This study underscores the potential of AI to transform hydroponic agriculture via intelligent resource management and environmental sustainability solutions.