Nectar distribution and its relation to food quality in honeybee (Apis mellifera) colonies

In honeybees (Apis niellifera), the process of nectar collection is considered a straightforward example of task partitioning with two subtasks or two intersecting cycles of activity: (1) foraging and (2) storing of nectar, linked via its transfer between foragers and food processors. Many observations suggest, however, that nectar colleclion and processing in honeybees is a complex process, involving workers of other sub-castes and depending on variables such as resource profitability or the amount of stored honey. It has been observed that food processor bees often distribute food to other hive bees after receiving it from incoming foragers, instead of storing it immediately in honey cells. While there is little information about the sub-caste affiliation and the behaviour of these second-order receivers, this stage may be important for the rapid distribution of nutrients and related information. To investigate the identity of these second-order receivers, we quantified behaviours following nectar transfer and compared these behaviours with the behaviour of average worker hive-bees. Furthermore, we tested whether food quality (sugar concentration) affects the behaviour of the second-order receivers. Of all identified second-order receivers, 59.3% performed nurse duties, 18.5% performed food-processor duties and 22.2% performed forager duties. After food intake, these bees were more active, had more trophallaxes (especially offering contacts) compared to average workers and they were found mainly in the brood area, independent of food quality. Our results show that the liquid food can be distributed rapidly among many bees of the three main worker sub-castes, without being stored in honey cells first. Furthermore, the results suggest that the rapid distribution of food partly depends on the high activity of second-order receivers.

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.