Development of new plant varieties with high yield potential and stress resistance includes identification of plants with better genes and phenotypes. It requires high-throughput and accurate measurements of plant dynamic responses to environmental variations, such as plant height [
1], canopy area and other morphological plant parameters [
2]. Conventionally plant phenotypes are measured manually in greenhouses and field conditions; this work is highly labor-intensive and time-consuming [
3]. Plant phenotyping has become an obstacle for the fast development of new crop varieties and underlies the link between genetic traits and environments [
4]. In recent years, various non-contact methods have been developed and tested to accelerate the measurement of plant geometric traits, including photogrammetry [
5], Light Detection and Ranging (LiDAR) [
1], Time-of-Flight (ToF) camera [
6] and Red-Green-Blue-depth (RGB-D) camera [
7]. For example, an imaging system was developed in [
5] to build two-dimensional (2D) mosaicked orthophotos for the measurement of leaf length and rosette area. Results showed that the relationship between the rosette area and total leaf expansion can be fitted with a power law function. However, the system must be calibrated for distortion to ensure true geometric quantities for measurement and is not capable of measuring plant height due to 2D image. A novel imaging and software platform was developed to measure three-dimensional root traits during the seedling development, and significant differences (
p-value < 0.05) were detected in morphological root traits of gellan gum-grown plants when grown using hydroponic and sand culture [
8]. Meanwhile, a high-precision laser scanning system was used in [
1] to reconstruct the architecture of the whole barley plant and take the measurements of the plant geometric dimensions. Results indicated that the laser scanner could estimate the plant height in the decimeter scale and the estimated parameters were highly correlated with the manually obtained parameters (
R2 = 0.85–0.97). However, it is expensive for high-throughput phenotyping using a LiDAR sensor because of the high-cost of LiDAR units and energy consumption [
9]. An automatic corn plant phenotyping system was proposed in [
6] with a ToF 3D camera. However, spatial resolution (SR) of ToF cameras is very low, thus it tends to be noisy and poorly calibrated for high-throughput phenotyping [
10]. Wang [
9] introduced a low-cost RGB-D camera system to estimate the size of fruits on trees from an inter row distance of approximately 2 m. The correlation between the manual measurement and machine vision-based estimation was accurate with
R2 = 0.96 and
Root Mean Square Error (
RMSE) = 4.9 mm for fruit length and
R2 = 0.95 and
RMSE = 4.3 mm for fruit width. However, the RGB-D camera performed poorly under direct sunlight, especially for measuring distance information at a large distance (3.5 m and above). Similar to ToF cameras, RGB-D cameras have low resolution in depth, for example, 640 × 480 pixels, at most [
11].
Relatively low-cost image-based systems can be useful due to the development of stereovision technology [
12]. Structure from Motion (SfM) enables three-dimensional (3D) models to be reconstructed using 2D images acquired from different view angles [
13]. Using SfM, the view angles are obtained by moving a single camera around an object of interest [
14], which brings the potential of using an easy and low-cost image-based system with a single camera to develop the 3D model of the object [
15]. Unlike classic photogrammetric methods which have high requirement in image position and resolution, there is no strict requirement for image overlap and resolution when using SfM based on automated image-based systems (e.g., the scale invariant feature transform (SIFT) of Lowe [
16]). The potential of image-based systems for 3D reconstruction and geometric measurement using SfM has been assessed for years in diverse fields, such as surface reconstruction in geoscience [
17,
18,
19], mapping or excavation in archaeology [
20,
21], and in forestry and agriculture [
22,
23]. Except for using the Unmanned Aerial Vehicle (UAV) image-based system mentioned above, SfM was also proven to be able to reconstruct fine parts of one or more plants in plant phenotyping. Santos [
24] showed that SfM can reconstruct branches and other fine structures of plants, and it took 111 min to process 143 images and 39 min for 77 images using the SIFT method. Jay [
14] obtained a strong linear correlation of the estimated plant height (
R2 = 0.99) and leaf area (
R2 = 0.94) with actual values. The measurement errors for height (vertical) and area (horizontal) were
RMSE = 11 mm,
Mean Absolute Error (
MAE) = 0.85 cm, and
RMSE = 85 cm,
MAE = 59 cm, respectively. Li [
25] established an SfM-MVS (structure-from-motion and multiple-view stereo) system in a greenhouse and able to get depth error of 14.86 mm for object within 1 m distance and of 10 mm for object with less than 800 mm distance.
However, there is no baseline information about the effects of image overlap, SR and camera parameters on geometric measurement accuracy and image processing efficiency using the SfM-based photogrammetric method. Therefore, the goal of this study was to evaluate the accuracy of geometric measurement in plants using the SfM method from sequential images acquired with an automated image-based platform. The specific objectives included (1) to evaluate the effects of image overlap and image resolution on the measurement accuracy and image processing efficiency; (2) to find a balance between processing time and required accuracy; (3) to verify the usability of the proposed method for the measurement of plant height.