Implementation of an IoT-Based High Efficiency and Low Maintenance Lettuce Hydroponic System

This study explores the development and evaluation of an Internet of Things (IoT)-based automated hydroponic system tailored for lettuce cultivation, aimed at optimizing plant growth while minimizing manual intervention. The system integrates Arduino Nano microcontrollers for managing pH and TDS sensors, transmitting data to a Raspberry Pi 4b for centralized data processing and pump control. Over a rigorous 5-day experimental period, the pH and TDS sensors demonstrated robust performance with average measurement errors of 3% and 4%, respectively, ensuring accurate monitoring of nutrient solution conditions. The automated system effectively maintained optimal pH levels (between 5.5 and 6.5) and TDS concentrations (ranging from 560 to 840 ppm) critical for lettuce growth. Additionally, leveraging the YOLOv5 model for image processing facilitated real-time monitoring and classification of lettuce growth stages, achieving a classification accuracy of 56%. This capability provides insights into plant development and facilitates timely intervention in cultivation practices. These results highlight the potential of IoT and machine learning technologies to enhance precision agriculture by ensuring precise monitoring, efficient resource management, and proactive decision-making in hydroponic farming. By reducing manual labor and optimizing resource utilization, such systems contribute to sustainable agricultural practices, addressing challenges posed by global population growth and urbanization. In conclusion, this research underscores the feasibility and benefits of integrating IoT and AI technologies in modern agriculture, paving the way for future advancements in automated farming systems that can sustainably meet global food demands