No abstract

Nowadays, smart irrigation is becoming an essential goal in agriculture, where water and energy are increasingly limited resources. Its importance will grow in the coming years in the agricultural sector where the optimal use of resources and environmental sustainability are becoming more important every day. However, implementing smart irrigation is not an easy task for most farmers since it is based on knowledge of the different processes and factors that determine the crop water requirements. Thanks to technological developments, it is possible to design new tools such as sensors or platforms that can be connected to soil-water-plant-atmosphere models to assist in the optimization and automation of irrigation. In this work, a low-cost, open-source IoT system for smart irrigation has been developed that can be easily integrated with other platforms and supports a large number of sensors. The platform uses the FIWARE framework together with customized components and can be deployed using edge computing and/or cloud computing systems. To improve decision-making, the platform integrates an irrigation model that calculates soil water balance and wet bulb dimensions to determine the best irrigation strategy for drip irrigation systems. In addition, an energy efficient open-source datalogger has been designed. The datalogger supports a wide range of communications and is compatible with analog sensors, SDI-12 and RS-485 protocols. The IoT system has been deployed on an olive farm and has been in operation for one irrigation season. Based on the results obtained, advantages of using these technologies over traditional methods are discussed.