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

Minhui Li,Redmond R Shamshiri,Cornelia Weltzien,Michael Schirrmann

doi:10.3390/agriculture11060563

Minhui Li, Redmond R Shamshiri + Show 2 more

Open Access

https://doi.org/10.3390/agriculture11060563

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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.

Highlights

Estimating the height and density of a plant canopy using 3D point clouds can help monitor the growth status of plants in the field
This study showed that the photogrammetric Structure from Motion (SfM)-Multi-View Stereo (MVS) method generated high quality 3D point clouds that were capable to accurately estimate leaf length and width of wheat plants when images were used from cameras viewing from different directions towards the canopy
In the case of a single camera view, the quality of the 3D point cloud was generally lower, and the degree of quality was highly dependent on which viewing angle was used towards the canopy

Summary

Introduction

Estimating the height and density of a plant canopy using 3D point clouds can help monitor the growth status of plants in the field. KGaA, Harsewinkel, Germany), YARA N-Sensor (ALS2, YARA International ASA, Oslo, Norway), GreenSeeker (Trimble Inc., Sunnyvale, CA, USA), and CropSpec (Topcon Corporation, Tokyo, Japan) provide canopy estimation from only one specific view [13,14], and missing significant information about the crop [15] To overcome these problems, various research works have studied modern approaches such as image-based [16], laser-based [17], and thermal imaging [18], depending on the data collection platform being used (i.e., ground-based or aerial-based [19]), these approaches usually suffer from the constraints and limitations of the field routes [20,21]

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