IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i15p1702-d1719146.html
   My bibliography  Save this article

Four-Dimensional Hyperspectral Imaging for Fruit and Vegetable Grading

Author

Listed:
  • Laraib Haider Naqvi

    (School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA)

  • Badrinath Balasubramaniam

    (School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602, USA)

  • Jiaqiong Li

    (School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA)

  • Lingling Liu

    (School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA)

  • Beiwen Li

    (School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA)

Abstract

Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types.

Suggested Citation

  • Laraib Haider Naqvi & Badrinath Balasubramaniam & Jiaqiong Li & Lingling Liu & Beiwen Li, 2025. "Four-Dimensional Hyperspectral Imaging for Fruit and Vegetable Grading," Agriculture, MDPI, vol. 15(15), pages 1-17, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1702-:d:1719146
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/15/1702/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/15/1702/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marwan Albahar, 2023. "A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities," Agriculture, MDPI, vol. 13(3), pages 1-22, February.
    2. Bo Xu & Xiang Cui & Wei Ji & Hao Yuan & Juncheng Wang, 2023. "Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5," Agriculture, MDPI, vol. 13(1), pages 1-18, January.
    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. Zohaib Khan & Yue Shen & Hui Liu, 2025. "ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions," Agriculture, MDPI, vol. 15(13), pages 1-36, June.
    2. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.
    3. Xiaowei Yu & Wei Ji & Hongwei Zhang & Chengzhi Ruan & Bo Xu & Kaiyang Wu, 2025. "Grasping Force Optimization and DDPG Impedance Control for Apple Picking Robot End-Effector," Agriculture, MDPI, vol. 15(10), pages 1-22, May.
    4. Jingyu Wang & Miaomiao Li & Chen Han & Xindong Guo, 2024. "YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection," Agriculture, MDPI, vol. 14(8), pages 1-20, July.
    5. Abdullah Addas & Muhammad Tahir & Najma Ismat, 2023. "Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    6. Imran Md Jelas & Nur Alia Sofia Maluazi & Mohd Asyraf Zulkifley, 2025. "An Attention-Enhanced Bottleneck Network for Apple Segmentation in Orchard Environments," Agriculture, MDPI, vol. 15(17), pages 1-28, August.
    7. Hang Zhou & Jin Gao & Fan Zhang & Junxiong Zhang & Song Wang & Chunlong Zhang & Wei Li, 2023. "Evaluation of Cutting Stability of a Natural-Rubber-Tapping Robot," Agriculture, MDPI, vol. 13(3), pages 1-23, February.
    8. Jinye Gao & Jun Sun & Xiaohong Wu & Chunxia Dai, 2025. "RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture," Agriculture, MDPI, vol. 15(13), pages 1-19, July.
    9. Shouwei Wang & Lijian Yao & Lijun Xu & Dong Hu & Jiawei Zhou & Yexin Chen, 2024. "An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields," Agriculture, MDPI, vol. 14(6), pages 1-16, May.
    10. Yi-Ming Qin & Yu-Hao Tu & Tao Li & Yao Ni & Rui-Feng Wang & Haihua Wang, 2025. "Deep Learning for Sustainable Agriculture: A Systematic Review on Applications in Lettuce Cultivation," Sustainability, MDPI, vol. 17(7), pages 1-33, April.
    11. Aichen Wang & Yuanzhi Xu & Dong Hu & Liyuan Zhang & Ao Li & Qingzhen Zhu & Jizhan Liu, 2025. "Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method," Agriculture, MDPI, vol. 15(13), pages 1-20, June.
    12. Shenghao Ye & Xinyu Xue & Shuning Si & Yang Xu & Feixiang Le & Longfei Cui & Yongkui Jin, 2023. "Design and Testing of an Elastic Comb Reciprocating a Soybean Plant-to-Plant Seedling Avoidance and Weeding Device," Agriculture, MDPI, vol. 13(11), pages 1-23, November.
    13. Long Su & Ruijia Liu & Kenan Liu & Kai Li & Li Liu & Yinggang Shi, 2023. "Greenhouse Tomato Picking Robot Chassis," Agriculture, MDPI, vol. 13(3), pages 1-23, February.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jagris:v:15:y:2025:i:15:p:1702-:d:1719146. 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.