Use of low-cost sensors for precision monitoring of agrifood systems.

This thesis focuses on the design, calibration, and validation of a low-cost system for monitoring soil moisture and temperature, with potential applications in the field of smart farming. The research was developed following a progressive and structured approach, starting from an analysis of the main challenges of modern agriculture, characterized by the increasing demand for food and the scarcity of natural resources, with particular attention to sustainable water management. In this context, the use of smart sensors and automated systems emerges as a key factor in improving production efficiency and reducing environmental impact. After outlining the regional agricultural framework and the main cropping systems, an analysis of the most relevant soil monitoring technologies was conducted, with a focus on systems for measuring soil moisture and nutrient content. Based on the strengths and limitations of the various solutions, the components most suitable for the implementation of the experimental system were selected. The developed system integrates environmental sensors (DHT22, SHT10) and soil sensors (SEN0308, DS18B20) to continuously acquire data on relative humidity, soil temperature, and raw ADC values, which are then converted into volumetric water content (VWC). Through an extensive statistical analysis, the system was characterized and calibrated, confirming the reliability of the devices and enabling the development of a soil-specific calibration equation, which improved the performance of soil moisture monitoring (R² = 0.99). Finally, the integration of data through FastAPI prepares the system for future remote data management and visualization, making it potentially suitable for field applications. However, in the current prototype version, remote data management has not yet been implemented. Therefore, the study confirms that low-cost sensors, when properly calibrated through gravimetric methods, can serve as reliable tools for real-time monitoring of soil water dynamics under operational conditions.