The good, the bad and the ugly: an open image dataset for automated sorting of good, bad, and imperfect produce using AI and robotics

In the face of the impending challenge of feeding a growing global population, one-third of all food produced ends up as waste. A notable contributor to this problem is the wastage of a third of perfectly edible and nutritious fresh produce because they need to meet the high cosmetic standards expected by consumers. Eliminating this wastage of imperfect produce is, therefore, a crucial and sustainable means to increase the food supply for a growing global population. This can be achieved through automated sorting of good, bad and imperfect produce using automation, robotics and machine vision. A prerequisite for such automated sorting is fast and accurate machine vision algorithms for successful differentiation between good, bad and imperfect produce. Training such algorithms requires large image datasets. While much work has gone into collecting images of good and bad produce, to the best of our knowledge, no such dataset exists for imperfect produce items. In this paper, we attempt to fill this gap by developing the first publicly available dataset of good, bad and imperfect produce items. The dataset has been made publicly available on the Harvard Dataverse for use in training machine vision algorithms for sorting good, bad and imperfect produce. It is our hope that this open dataset will contribute to improving research and practice for sorting and saving imperfect produce in the food supply chain.