FruityHub: A diverse collection of fruits created for edibility estimation

The export of fresh fruits not only exerts a
substantial influence on both national and global economies but
also serves as a crucial source of livelihood for a vast number of
farmers. Automated estimation of these fruits’ edibility (i.e.
freshness level) is very essential to maintain the quality of
exported fruits and to reduce the financial loss. Owing to the
over-whelming performances achieved by deep neural networks
in performing image classification tasks in recent years, many
researchers have designed automated methods by exploiting the
deep neural architectures to perform non-invasive quality
identification of several plant parts (leaves, fruits, vegetables,
etc.) in more time-efficient and cost- effective manner. The most
notable hindrance which is faced by researchers while designing
the automated methods is the lack of proper databases which
are required to train them. To mitigate this research gap, in this
work we have created a novel database namely, FruityHub
comprising of a total of 1786 image frames belonging to eight
different types of fruits namely, Dragon Fruits, Net Melons,
Strawberries, Star Fruit, Zucchini, Mango, Pineapple and
Plum. The image frames belonging to each fruit category can be
classified as Fresh, Damaged and Severely Damaged which
facilitates automated estimation of edible quality of these fruits
depending upon their skin color, texture, shape, etc