Cathrine Baungaard, Katie E Lane, Lucinda Richardson
Understanding nutrition students' knowledge, perceived barriers and their views on the future role of nutritionists regarding sustainable diets
Nutrition bulletin
nuts nutrientdense foods incorporated healthy diet artificial intelligencepowered diettracking apps may promote nut consumption providing realtime accurate nutrition information depend data model availability team developed dataset comprising 1380 photographs rgb color format resolution 4032 3024 pixels images feature 11 types nuts commonly consumed photo includes three nut types type consists 24 nuts 69 nuts image rectangular bounding boxes drawn using visual geometry group vgg image annotator facilitate identification nut delineating locations within images approach renders dataset excellent resource training models capable multilabel classification object detection meticulously divided training validation test subsets utilizing transfer learning python icevision framework deep neural network models adeptly trained recognize pinpoint nuts depicted photographs ultimate model exhibited mean average precision 07596 identifying various nut types within validation subset demonstrated 979 accuracy rate determining number kinds nuts present test subset integrating specific nutritional data type nut model precisely error margins ranging 08 26 calculate combined nutritional contentencompassing total energy proteins carbohydrates fats total saturated fiber vitamin e essential minerals like magnesium phosphorus copper manganese seleniumof nuts shown photograph dataset model made publicly available foster data exchange spread knowledge research underscores potential leveraging photographs automated nut calorie nutritional content estimation paving way creation dietary tracking applications offer realtime precise nutritional insights encourage nut consumption