Towards Smart Farming Through Machine Learning-Based Automatic Irrigation Planning

Abstract

The growing scarcity and strong demand for water resources require an urgent policy of measures to ensure the rational use of these resources. Farmers need irrigation planning and rationalization tools to be able to take advantage of scientific know-how, especially artificial intelligence tools, to improve the management of water use in their farming irrigation practices. To improve water management in irrigated areas, models for estimating future water needs are needed. The objective of this work is to estimate the water needs of crops for efficient management of irrigation networks and planning of the use of hydraulic resources. In this regard, data-driven machine learning algorithms can be employed for water resources monitoring and governance. These methods, derived from artificial intelligence, have obtained promising results in the planning, management, and control of water resources. To do this, we prepare a dataset with information about the appropriate attributes for calculating water requirements. The proposed approach begins with a cleaning of the data set to effectively predict water needs. The process of extracting relevant data is based on a combined tool for data mining and knowledge discovery on irrigation and water needs. We then validate the effectiveness of the various data mining algorithms used and of certain traditional methods of estimating evapotranspiration (ETc) to predict water requirements, in particular the Water balance (WB), the Penman-Monteith method (FAO PM) adopted by the Food and Agriculture Organization of the United Nations, and the Bowen-Energy Balance Report (BREB). Some of the algorithms used include XGBoost, Random Forest, and Deep Artificial Neural Networks. Currently, innovations can be consolidated to minimize costs and maximize the use of resources.