## 1 Introduction
In general, concentration of VOO phenolic compounds decreases as water availability in the soil increases, but inconsistent effects of irrigation on the olive oil phenolic compounds, as well as on volatile compounds, are found in the published literature [9; 15; 16; 17; 18], probably due to very complex response of olive oil composition to irrigation which also depends on cultivar, growing seasons as well as environmental conditions. This highlights the importance of long-term studies on these issues. There is only a small percentage of intensive production olive orchards in Croatia [5] and irrigation systems are commonly implemented in such orchards as a strategy to increase quality and yield of olive oils. Due to limited water resources on the Croatian islands, a smart choice in olive tree watering regimes (for example, deficit irrigation) is of importance. Deficit irrigation strategies are based on supplying irrigation quantities below the irrigation needs for the maximum potential crop evapotranspiration (ET\({}_{\text{C}}\)), but with minimized impact of irrigation on crop performance [18]. Defined application of a deficit irrigation strategy in olive orchards is needed not only to improve oil yield, but to also obtain high quality olive oil. Moreover, olive growers experience difficulties in determining irrigation outbreak, water quantity and application rate which frequently results in overdue or unbalanced irrigation. This, on the other hand, can lead to unnecessarily reduced yield as well as increased production costs and negative environmental effects. Current technologies in olive cultivation, as well in other crop production, increasingly rely on the \"smart agriculture\" concept. Introducing smart agriculture technologies and using autonomous support systems can, on the one hand, compensate for a lack of workforce, while advanced sensor-based technology can, on the other hand, enable precise and automatic water rate application, thus increasing water usage efficiency as well as the yield and the quality of the crop. Additionally, with the help of artificial intelligence (AI), algorithms can be used to filter the data directly derived from measuring various factors related to the plant, soil or environment, which will help the producer to make informed farm management decisions [19]. Under the Smart Agriculture Network (SAN) project, an AI model has been developed; a simple application tool with the aim of helping producers to make decisions related to the irrigation run time and water rate was tested for the first time in this study [20]. This 'Internet of things' (IoT) tool was developed to determine irrigation rate using the standard rate calculation method and involved irrigation based on plant phenological stage. Taking into consideration the main phenological stages' water requirements, 80% of the total water requirement was applied before flowering, fruit growth and oil accumulation, while 50% of the required water was applied between these phenological stages. The goal was to provide enough water in critical growth and development stages, while reducing the total volume of added water in the conditions of limited water supply. In light of the above, the aim of the present study was to investigate the influence of four different watering regimes applied during two years on the Coratina cv. olive trees grown on karst soils on the Croatian island, Dugi otok, with respect to the quality, composition and quantity of the obtained olive oil. Investigated watering regimes were rainfed conditions, deficit irrigation that represents the usual producer's practice, deficit irrigation acquired by SAN technology in respect to phenological stages, and irrigation with 100% of ET\({}_{\text{c}}\) level. Island Dugi otok is known for excessive dryness with elevated ET values [21]. To date, there is a lack of data regarding the influence of irrigation on Coratina cv. olive trees grown on karst soil in Croatia.