IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6411-d1443802.html
   My bibliography  Save this article

The Good, The Bad and The Ugly: An Open Image Dataset for Automated Sorting of Good, Bad, and Imperfect Produce Using AI and Robotics

Author

Listed:
  • Anjali Sharma

    (The Roeper School, Birmingham, MI, USA
    LIME Lab Low Profit LLC, Detroit, MI, USA)

  • Vikas Kumar

    (Faculty of Business, Law and Social Sciences, Birmingham City University, Birmingham, UK
    Department of Management Studies, Graphic Era Deemed to be University, Dehradun, India)

  • Laxmi P. Musunur

    (Fanuc America, Rochester Hills, MI, USA)

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 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.

Suggested Citation

  • Anjali Sharma & Vikas Kumar & Laxmi P. Musunur, 2024. "The Good, The Bad and The Ugly: An Open Image Dataset for Automated Sorting of Good, Bad, and Imperfect Produce Using AI and Robotics," Sustainability, MDPI, vol. 16(15), pages 1-24, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6411-:d:1443802
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6411/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/15/6411/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bolos, Laura Andreea & Lagerkvist, Carl Johan & Nayga, Rodolfo M. Jr., 2019. "Consumer Choice and Food Waste: Can Nudging Help?," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 34(1), February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Vittuari & Luca Falasconi & Matteo Masotti & Simone Piras & Andrea Segrè & Marco Setti, 2020. "‘Not in My Bin’: Consumer’s Understanding and Concern of Food Waste Effects and Mitigating Factors," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
    2. Van Loo, Ellen J. & Caputo, Vincenzina & Lusk, Jayson L., 2020. "Consumer preferences for farm-raised meat, lab-grown meat, and plant-based meat alternatives: Does information or brand matter?," Food Policy, Elsevier, vol. 95(C).
    3. Pasirayi, Simbarashe & Fennell, Patrick B. & Sen, Argha, 2023. "The effect of third-party delivery partnerships on firm value," Journal of Business Research, Elsevier, vol. 167(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6411-:d:1443802. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.