Determination of stem position and height of reconstructed maize plants using a time-of-flight camera

Vázquez-Arellano, Manuel; Paraforos, Dimitrios S.; Reiser, David; Izard, Miguel Garrido; Griepentrog, Hans W.

doi:10.1016/j.compag.2018.09.006

Cited by 28 publications

(17 citation statements)

References 17 publications

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“…The bivariate point density histogram enabled the detection of the peak of laser hits for each individual tree ( Figure 5). The assumption that the stem points are concentrated in the center of the plant was based on previous studies [8,24]. Consistently, the area closer to the stem position was assumed to appear with enhanced frequency.…”

Section: Separation Of Trees By Means Of the Stem Positionmentioning

confidence: 90%

“…The methodology for determining the stem position relied on the previous study by Vazquez-Arellano and co-workers [24]. A first-order polynomial curve considering the least absolute residuals (LAR) was used to connect the reference stem positions in each row (Equation 13)…”

Section: Tree Row Alignmentmentioning

confidence: 99%

“…The removal of soil points by contrast led to a reduced mean distance error of 81.6 mm with a 21.4 mm SD and lower RMSE of 0.71% as well as an MBE of 5.2 mm. As a result, for the case of the removal of soil points, low measuring uncertainty affecting the overlapping process was found [24,35]. The coordinates of the estimated stem position were compared with the stem position, which was defined with the RTK-GNSS ( Figure 6).…”

Section: Separation Of Trees By Means Of the Stem Positionmentioning

confidence: 99%

“…Nevertheless, this error did not reveal an underestimation of the height (Table 4). Arellano et al [24], with a similar method, determined the stem position in maize and acquired the height profile of the plants pointing to a MAE of 7.3 mm from the actual height. Walnuts trees were segmented and their height was estimated considering the Min and Max values in the z axis, resulting in a significant relationship with the manual measurements (R 2 = 0.95) [36].…”

Section: Estimation Of Tree Variablesmentioning

confidence: 99%

“…In another study, the 2D histogram was used to obtain the height profile in 3D point clouds of cotton plants, resulting in a strong correlation (R 2 = 0.98) with manual measurements [23]. Moreover, the stem position in maize plants was determined based on bivariate point density histograms in order to find the regional maxima [24]. Thus, single plant segmentation was carried out by projecting a spatial cylindrical boundary around the estimated stem positions of plant point clouds.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR

et al. 2019

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Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard.

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Section: Separation Of Trees By Means Of the Stem Positionmentioning

confidence: 90%

Section: Tree Row Alignmentmentioning

confidence: 99%

Section: Separation Of Trees By Means Of the Stem Positionmentioning

confidence: 99%

Section: Estimation Of Tree Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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