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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1439086
This article is part of the Research Topic AI, Sensors and Robotics in Plant Phenotyping and Precision Agriculture, Volume III View all 11 articles

NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology

Provisionally accepted
Hong-Beom Choi Hong-Beom Choi Jae-Kun Park Jae-Kun Park Soo Hyun Park Soo Hyun Park Taek-Sung Lee Taek-Sung Lee *
  • Gangneung Institute of Natural Products, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea

The final, formatted version of the article will be published soon.

    Recent advancements in digital phenotypic analysis have revolutionized the morphological analysis of crops, offering new insights into genetic trait expressions. This manuscript presents a novel 3D phenotyping pipeline utilizing the cutting-edge Neural Radiance Fields (NeRF) technology, aimed at overcoming the limitations of traditional 2D imaging methods. Our approach incorporates automated RGB image acquisition through unmanned greenhouse robots, coupled with NeRF technology for dense Point Cloud generation. This facilitates non-destructive, accurate measurements of crop parameters such as node length, leaf area, and fruit volume. Our results, derived from applying this methodology to tomato crops in greenhouse conditions, demonstrate a high correlation with traditional human growth surveys. The manuscript highlights the system's ability to achieve detailed morphological analysis from limited viewpoint of camera, proving its suitability and practicality for greenhouse environments. The results displayed an R-squared value of 0.973 and a Mean Absolute Percentage Error (MAPE) of 0.089 for inter-node length measurements, while segmented leaf point cloud and reconstructed meshes showed an R-squared value of 0.953 and a MAPE of 0.090 for leaf area measurements. Additionally, segmented tomato fruit analysis yielded an R-squared value of 0.96 and a MAPE of 0.135 for fruit volume measurements. These metrics underscore the precision and reliability of our 3D phenotyping pipeline, making it a highly promising tool for modern agriculture.

    Keywords: 3D phenotyping, Neural Radiance Fields, Automated growth measurement, point cloud, Greenhouse Application

    Received: 27 May 2024; Accepted: 04 Oct 2024.

    Copyright: © 2024 Choi, Park, Park and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Taek-Sung Lee, Gangneung Institute of Natural Products, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.