Design and testing of Isadora+: A decision-support system for estimating irrigation requirements using selectable agronomic methods

Climate projections indicate increasing evaporative demand and greater variability, as well as potential reductions in rainfall, across major fruit-producing regions. Under these conditions, decision-support tools capable of accurately estimating crop water requirements are becoming essential for modern fruit production. This study presents Isadora+, a digital irrigation decision-support system (DSS) that we developed and evaluated to estimate crop water requirements using state-of-the-art methodologies. In this study, the DSS was evaluated using two evapotranspiration-based approaches: the FAO-56 crop coefficient method and the Allen and Pereira method. The DSS integrates a cloud-based backend, a RESTful API, and a mobile application to deliver irrigation recommendations to end users. Its modular architecture enables real-time data acquisition, automated data processing, and the dynamic generation of irrigation recommendations expressed as irrigation duration for each irrigation sector, tailored to specific orchard conditions. We first describe the Isadora+ DSS and then present results from a validation exercise in which the platform was used to estimate irrigation requirements (IR) for a super-high-density olive orchard using the two mentioned approaches. Daily IR values were generated by Isadora+ over two consecutive irrigation seasons and compared with manual calculations. We also assessed the impact of using weather data recorded by a nearby agrometeorological station versus data provided by the Open-Meteo API, a web-based open-source meteorological data service. Our results demonstrate that Isadora+ provides growers and technical advisors with a robust and scalable tool for estimating the crop IR, requiring minimal human intervention and without reliance on local weather station records.