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Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds

Author

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  • Minhui Li

    (Agromechatronics, Technische Universität Berlin, Straße des 17, Juni 144, 10623 Berlin, Germany
    Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany)

  • Redmond R. Shamshiri

    (Agromechatronics, Technische Universität Berlin, Straße des 17, Juni 144, 10623 Berlin, Germany
    Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany)

  • Michael Schirrmann

    (Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany)

  • Cornelia Weltzien

    (Agromechatronics, Technische Universität Berlin, Straße des 17, Juni 144, 10623 Berlin, Germany
    Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany)

Abstract

Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat ( Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at −45° and horizontally at 0° (VA −45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment.

Suggested Citation

  • Minhui Li & Redmond R. Shamshiri & Michael Schirrmann & Cornelia Weltzien, 2021. "Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds," Agriculture, MDPI, vol. 11(6), pages 1-17, June.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:6:p:563-:d:578298
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    References listed on IDEAS

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    1. Federico Pallottino & Paolo Menesatti & Simone Figorilli & Francesca Antonucci & Roberto Tomasone & Andrea Colantoni & Corrado Costa, 2018. "Machine Vision Retrofit System for Mechanical Weed Control in Precision Agriculture Applications," Sustainability, MDPI, vol. 10(7), pages 1-9, June.
    2. Jinhai Cai & Pankaj Kumar & Joshua Chopin & Stanley J Miklavcic, 2018. "Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-21, May.
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