  Fruit is a key crop in worldwide agriculture feeding millions of people. The
standard supply chain of fruit products involves quality checks to guarantee
freshness, taste, and, most of all, safety. An important factor that determines
fruit quality is its stage of ripening. This is usually manually classified by
field experts, making it a labor-intensive and error-prone process. Thus, there
is an arising need for automation in fruit ripeness classification. Many
automatic methods have been proposed that employ a variety of feature
descriptors for the food item to be graded. Machine learning and deep learning
techniques dominate the top-performing methods. Furthermore, deep learning can
operate on raw data and thus relieve the users from having to compute complex
engineered features, which are often crop-specific. In this survey, we review
the latest methods proposed in the literature to automatize fruit ripeness
classification, highlighting the most common feature descriptors they operate
on.
