  Key role in the prevention of diet-related chronic diseases plays the
balanced nutrition together with a proper diet. The conventional dietary
assessment methods are time-consuming, expensive and prone to errors. New
technology-based methods that provide reliable and convenient dietary
assessment, have emerged during the last decade. The advances in the field of
computer vision permitted the use of meal image to assess the nutrient content
usually through three steps: food segmentation, recognition and volume
estimation. In this paper, we propose a use one RGB meal image as input to a
multi-task learning based Convolutional Neural Network (CNN). The proposed
approach achieved outstanding performance, while a comparison with
state-of-the-art methods indicated that the proposed approach exhibits clear
advantage in accuracy, along with a massive reduction of processing time.
