One of the goals of the iFishIENCi project is to bring to market the iFishIENCi Biology Online Steering System (iBOSS) that significantly improves production control and management for all fish aquaculture systems. Within iBOSS, smart functions can be implemented to take advantage of data from multiple sources to give better insight for the farmer, optimize production, better production planning, etc. The Report on final Smart feeding AI aims to implement one such smart function: iBOSS Smart Feeding. By using an AI approach, it is expected appropriate correlations between fish behaviour, feeding regime and water quality can be found, and used for controlling feeder and achieve optimal feeding operation. While a lot of data is available, it has not yet been explored how to concatenate this into one model for optimizing feeding efficiency. Thus, an approach to integrate multiple sources of feeding efficiency quantification in to one, is proposed.