IoT Integrated Hydroponic System for Precision Agriculture

Abstract

The integration of Machine Learning (ML) and the Internet of Things (IoT) in agriculture has transformed traditional farming by enabling intelligent, automated systems that enhance productivity, reduce resource wastage, and support data-driven decision-making. Traditional farming methods often involve manual monitoring of critical environmental parameters, resulting in inefficiencies, inconsistent crop yields, and human error. This paper presents the design and implementation of an ML and IoT-based smart hydroponic farming system aimed at optimizing crop cultivation through automated environmental monitoring and control. The primary objective of the proposed system is to regulate key parameters like pH level, nutrient concentration, temperature, humidity, light intensity, and air quality in real time using Arduino and ESP32 microcontrollers interfaced with various sensors and relay-controlled actuators. An ESP32 camera is employed for ML-based image processing to detect plant freshness and growth anomalies, enabling timely corrective actions. The system features an IoT-enabled dashboard for remote monitoring and control, empowering farmers to make informed decisions regardless of their location. By analyzing sensor data and predicting environmental fluctuations, the system proactively maintains optimal conditions for plant growth. The proposed solution ensures sustainability, reduces manual labor, and improves overall farming efficiency. Furthermore, it lays the groundwork for future enhancements such as AI-driven predictive analytics, cloud integration, and robotic automation to advance the scalability and resilience of modern farming practices and precision agriculture.