Protocol To Access Produce Images from the Imperfect Foods Dataverse on the Harvard Dataverse v1

Abstract

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 do not meet the high cosmetic standards expected by consumers. Eliminating this wastage of imperfect produce is therefore a crucial and sustainable means to increase 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 are 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. To fill this gap I have curated a dataset of good, bad, and imperfect produce items for a wide variety of fruits and vegetables. At this time of writing of this version of the protocol, images have been collected for just over 30 produce items and new ones are continuously being added. 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 of imperfect produce in the food supply chain. In this protocol, we provide instructions on how to access these images for interested researchers and practitioners.