Food computing
This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators.
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Food computing is currently a fast-growing field of research. Web mining and content analysis are also increasingly essential in this field, especially for recognising food entities.
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In the catering industry, one major challenge is the unknown short-term demand for dish portions. Meeting these demands is important for the industry but predicting future sales is a challenging task.
This data set presents sales of food portions from a canteen in absolute numbers of dish portions per day. In particular, the columns include text-based extractions of ingredients and a date. The data set is intended to be used for forecasting/predicting the food portions on a daily level.
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Images of various foods, taken with different cameras and different lighting conditions. Images can be used to design and test Computer Vision techniques that can recognize foods and estimate their calories and nutrition.
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