IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i15p1574-d1707504.html
   My bibliography  Save this article

Phenotypic Trait Acquisition Method for Tomato Plants Based on RGB-D SLAM

Author

Listed:
  • Penggang Wang

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China)

  • Yuejun He

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China)

  • Jiguang Zhang

    (Institute of Automation of Chinese Academy of Sciences, Beijing 100090, China)

  • Jiandong Liu

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China)

  • Ran Chen

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China)

  • Xiang Zhuang

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China)

Abstract

The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union ( mIoU ) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error ( RMSE ) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making.

Suggested Citation

  • Penggang Wang & Yuejun He & Jiguang Zhang & Jiandong Liu & Ran Chen & Xiang Zhuang, 2025. "Phenotypic Trait Acquisition Method for Tomato Plants Based on RGB-D SLAM," Agriculture, MDPI, vol. 15(15), pages 1-25, July.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1574-:d:1707504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/15/1574/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/15/15/1574/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:1574-:d:1707504. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.