  Dietary studies showed that dietary-related problem such as obesity is
associated with other chronic diseases like hypertension, irregular blood sugar
levels, and increased risk of heart attacks. The primary cause of these
problems is poor lifestyle choices and unhealthy dietary habits, which are
manageable using interactive mHealth apps. However, traditional dietary
monitoring systems using manual food logging suffer from imprecision,
underreporting, time consumption, and low adherence. Recent dietary monitoring
systems tackle these challenges by automatic assessment of dietary intake
through machine learning methods. This survey discusses the most performing
methodologies that have been developed so far for automatic food recognition
and volume estimation. First, we will present the rationale of visual-based
methods for food recognition. The core of the paper is the presentation,
discussion and evaluation of these methods on popular food image databases.
Following that, we discussed the mobile applications that are implementing
these methods. The survey ends with a discussion of research gaps and open
issues in this area.
