The strategic importance of the food industry and the rapid transition to the third food regime - which aims to streamline and increase the efficiency and profitability of the entire chain in the food industry - attracts continued efforts both from academia and the industry for the digitisation of the sector. This movement is also supported by appliance manufacturers and it often involves integrating techniques from IoT (Internet of Things), Machine Learning (ML) and Computer Vision (CV). Previous efforts, either commercial or academic [LFC18; Sha+19], have been made in terms of deploying smarter appliances (i.e. smart fridges). However, the scope is usually limited to either detecting a small number of ingredients or providing remote connectivity (basic sensor information and control). In recent years, the need to provide users with readily available alternatives for a healthier lifestyle, in this case through a balanced diet, has incentivised research towards recipe recommender systems that take into account the user’s health [GRM15; ST21; TRH17]. Moreover, simply recommending recipes based on available ingredients or through collaborative filtering has proven to be insufficiently informed, leading to more complex requirements needed to be met by modern recipe recommender systems which must take into account multiple knowledge sources and user-centered constraints [Mus+20; NL21]. Food logging has already been explored at the level of smartphone applications where the users type each item in their daily intake. All the above topics seem to be on an increasing trend in terms of interest and invested effort.