| Summary: | Nowadays, the saving of natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. In this topic the Internet of Things has been highlighted, these solutions are characterized by offering robustness and simplicity, while being low cost. In this dissertation was presented the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, based on previous studies at the level of modules and communication protocols used, a mobile application for iOS that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. In order to adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration, of the studied algorithms (Decision Trees, Extreme Gradient Boosting (XGBoost), Random Forest, Neural Networks and Support Vectors Machines) the one that obtained the best results was XGBoost, presenting results of 87.73% of accuracy. Besides the ML solution, a script was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.
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