DEVELOPMENT AND VALIDATION OF A LOW-COST AND SUSTAINABLE AUTOMATED SYSTEM FOR OPTIMIZING THE USE OF WATER RESOURCES IN AGRICULTURAL IRRIGATION PROCESSES
Automated irrigation; Watermark sensors; matric potential; ESP32; precision agriculture.
The increasing pressure on global water resources, intensified by climate change, agricultural expansion, and the need for higher food production, highlights the urgency of developing accessible and efficient technologies for sustainable irrigation management. In Brazil, irrigation accounts for more than 70% of freshwater consumption, and losses exceeding 40% remain common due to inadequate management practices. In this context, this thesis is justified by the need for low-cost, technically robust solutions that can be adopted by small and medium-sized farms, integrating sensors, automation, and connectivity to support more precise and sustainable irrigation decisions. The main objective of this work is to develop, calibrate, and validate a complete monitoring and automation system for irrigation, composed of two complementary components: (1) an embedded system for measuring soil matric potential using Watermark 200SS sensors integrated with the Arduino platform, and (2) a low-cost automated irrigation system based on the ESP32 microcontroller, a flow sensor, and Internet of Things (IoT) technologies, capable of operating by time or volume. The central hypothesis is that low-cost embedded systems, when properly calibrated, can achieve accuracy comparable to commercial equipment, enabling reliable matric potential measurements and efficient volumetric irrigation control. The methodology included physical–hydric soil characterization using the Richards method and the Van Genuchten model, the development of a conditioning circuit with pseudo-AC excitation for reading the electrical resistance of Watermark sensors, electronic calibration using standard resistors, and the application of correction factors to improve measurement accuracy. In parallel, an automated irrigation system was developed using an ESP32 controller, hydraulic pump, solenoid valve, and flow sensor, and experimentally validated under different hydraulic conditions. Measurements were compared with a hydrometer and a graduated beaker to assess precision, repeatability, and stability. The expected results include a strong correlation between the embedded system and the digital reference meter for matric potential, significant reduction of measurement errors after segmented calibration, increasing accuracy of the irrigation system for durations above 20 seconds, and high stability under higher-flow conditions. The thesis demonstrates that low-cost solutions can be reliably integrated into irrigation management, contributing to water conservation, agricultural automation, and the democratization of precision agriculture. Future integration with meteorological APIs and evapotranspiration models is expected to enable the development of a fully automated intelligent irrigation system, further enhancing sustainability and decision-making efficiency.
Sustainable Development Goals: #2 - Zero Hunger; 3# - Good Health and Well-being; #8 - Decent Work and Economic Growth; #9 - Industry, Innovation and Infrastructure; #11 - Sustainable Cities and Communities; #12 - Responsible Consumption and Production.
Social Impact
• Democratization of access to irrigation technology
• Reduction of water waste
• Strengthening of family farming
• Improvement of working conditions in rural areas
• Digital inclusion of small-scale farmers
• Contribution to food security
• Support for sustainability and the Sustainable Development Goals (SDGs)