## 1 Introduction  
Agriculture in the world plays an important role in feeding the population. Agriculture has recently been threatened by the availability of water and has generated concern about the use of water in the world, leaving much to be desired in agriculture as it has generated an unsustainable situation [1] For this reason, smart irrigation systems were created to optimize water use in crops and improve productivity [2]. The development of the 21 st century is still largely based on agriculture with activities in agribusiness, taking into consideration climate change, soil and irrigation factors in almost all regions, for this, a study [3] was conducted using artificial neural networks (ANN) that analyzes the smart agriculture dataset with parameters such as temperature, soil moisture, wind speed, solar radiation and soil water tension. Agriculture has played an important role in the development of every country, therefore, automation has been increased the area of irrigation with smart technology in some cases through embedded systems that concentrates on controlling the irrigation process automatically using a Raspberry Pi device through Python programming with the help of moisture sensors that monitor the soil [4]. The level of irrigation depends on the soil moisture content and crop type, which reduces the overall energy consumption and optimizes the use of water reserves, a low-cost Arduino-based smart irrigation solution [5]. The advancement in automation techniques allows improving crop yields, making them more profitable and reducing irrigation wastage by having sensors deployed in an agricultural field, which sends data through a microprocessor via Internet of Things (IoT) devices with the cloud, through a decision tree algorithm, which sends an alert by mail to farmers and allows the decisions on water supply to be made in advance [6]. Automation through irrigation scheduling has been achieved by several methods, such as the measurement of environmental variables used for control, simulation of irrigation strategies that allows forecasting conditions for crops without damaging the crops and then being applied in the field [7]. Automated irrigation is based on parameters monitored from a meteorological station that indicates parameters such as crop water demand, soil, evapotranspiration, precipitation, climatic conditions, etc., which are used to analyze the data in various scenarios and to propose strategies and control methods where irrigation efficiency analysis can be applied [8]. Through simulation, the performance of proposals can be validated, so [9] shows a control algorithm with a database that forecasts crop yield and manages water resources, closed-loop control is performed and through feedback from weather conditions the model anticipates the crop water demand and to generate the irrigation scheduling of water consumption [10]. Although water resources are available in many countries, global warming, pollution and losses due to the misapplication of water are considerable and inappropriate types of risks have been generated, affecting the efficient use of water [11]. Agricultural irrigation is mostly done manually/mechanically without considering the amount of water needed for the crop. With the help of technology, it is possible to create smart irrigation systems that focus on saving water and energy and obtaining a higher quality in the final product [12]. Different projects have been carried out in different sectors of smart irrigation systems with fuzzy logic based on rules, by sensing soil moisture and meteorological variables and managing the flow of water through an agronomic design [12]. The smart irrigation design in vegetables implemented in a university is managed through a graphical interface, allowing the automation of irrigation using fuzzy logic compared to manual drip irrigation [13].