IoT-Based Multi-Sensor Data Fusion for Precision Crop Yield Optimization Using Arduino, ESP32, and Webcam Integration

Abstract

The increasing global population necessitates a critical shift toward advanced, efficient agricultural practices to secure food production. Traditional farming methods, which rely heavily on subjective manual assessment, are often insufficient, leading to suboptimal resource utilization and slow adaptation to changing environmental dynamics. This paper details the development  and realization of an innovative Internet of Things (IoT) system specifically developed for precision crop yield optimization. Utilizing an Arduino -Based controller as the central computing module, processing unit, the system integrates a comprehensive array of five critical sensors to monitor essential environmental metrics in real-time: air temperature, relative humidity, rainfall levels, soil moisture content, and carbon dioxide (CO₂) concentration. The aggregated data stream is instantly transmitted to the Things Speak cloud platform, facilitating immediate data visualization, secure storage, and advanced temporal analysis. This continuous, data-driven intelligence empowers agricultural stakeholders to make timely and precise decisions regarding irrigation schedules, microclimate regulation, and early disease threat mitigation. By leveraging a multi-sensor data fusion approach, this solution offers a holistic understanding of the field environment, representing a significant technological upgrade over conventional techniques and promoting enhanced operational efficiency, sustainability, and higher crop yields.

Keywords:

Internet of Things (IoT),

Precision Agriculture,

Multi-Sensor Fusion,

Crop Yield Optimization,

Things Speak.