Method for estimating rice plant height without ground surface detection using laser scanner measurement

Phan, Anh Thu Thi; Takahashi, Kazuhiko; Rikimaru, Atsushi; Higuchi, Yasuhiro

doi:10.1117/1.jrs.10.046018

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…Here, we assume that scanning angle effects can be ignored. As reported in our previous study, 28 the relative vertical distance (rD), computed directly from the laser data without considering the ground-surface level, was related to H and was insignificantly affected by the planting density. Therefore, under the practical scenario of this study, we normalize D not by H but by the respective rDs derived from the laser data at each observation time.…”

Section: Computation Of Relative Spatial Volume From the Observed Lassupporting

confidence: 68%

“…In our previous study, we computed D of the scanning points by multiplying the observed range data by the cosine of the corresponding inclination angle. 28 Here, D denotes the position of the laser pulse on the rice canopy above the ground surface. Of course, the range of D depends on the observation date because H increases over time.…”

Section: Data Extractionmentioning

confidence: 99%

“…For this purpose, the tops and bottoms of the rice plants were identified directly from the observed laser data. Phan et al 28 identified the top of the rice plant (D top ) as the first percentile height. To avoid the effects of scanning points located on or just above the ground surface, we define the bottom of the rice plant (D bottom ) by three rice bottom positions at the 70th, 80th, and 95th percentile heights (D 70 , D 80 , and D 95 , respectively).…”

Section: Computation Of Relative Spatial Volume From the Observed Lasmentioning

confidence: 99%

“…[23][24][25][26][27] In our previous study, we introduced a method for estimating the heights of rice plants from laser data without requiring the ground-surface level. 28 However, few studies have attempted the monitoring of rice-plant stem number. Therefore, a method for estimating rice-plant stem number is demanded for monitoring rice growth and predicting the rice yield potential.…”

Section: Introductionmentioning

confidence: 99%

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Fundamental study for estimating rice-plant stem number using laser scanner measurements

Phan

Rikimaru

Higuchi

et al. 2017

J. Appl. Remote Sens

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The number of rice-plant stems (S), directly affecting the competition among rice plants and contributing to rice yield, is estimated from laser data. The laser data were normalized to eliminate the increasing plant height effect. Relative spatial volume (rV laser s) was derived and scaled as an exponential function of S. A relationship between rV laser s and S is confirmed in two growing seasons (2014 and 2016, separately obtained by two different laser scanners). The scaling and exponent factors (β and α) depended on the planting geometry, planting density, and bottom position of the rice plants (D bottom) but were almost independent of the number of divided layers in the rV laser s computation. From the estimated stem number using D bottom at the 80th percentile (D 80), the maximum S was obtained at ∼50 days after transplanting. In both years, the relative error in the estimate was below 0.10, and the bias was small. In the models with D 80 in 2014 (MD 80 2014) and D 95 in 2016 (MD 95 2016), the β and α values were very similar. Using the rV laser s measure, we can disregard the footprint characteristics and voxel size. The presented results support the proposed approach as a useful future method for estimating rice-plant stems. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Computation Of Relative Spatial Volume From the Observed Lassupporting

confidence: 68%

Section: Data Extractionmentioning

confidence: 99%

Section: Computation Of Relative Spatial Volume From the Observed Lasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fundamental study for estimating rice-plant stem number using laser scanner measurements

Phan

Rikimaru

Higuchi

et al. 2017

J. Appl. Remote Sens

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“…By establishing the estimation model, which is the function of feed amount and crop volume, they accurately calculated crop density. In order to acquire the rice density, Phan et al [18] put forward a method that divided the voxels of rice point-cloud data and calculated the spatial volume. As the spatial volume depends on the height of the rice and the numbers of plants, the rice density could be measured by inverse transformation after removing the influence of rice height.…”

Section: Introductionmentioning

confidence: 99%

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

et al. 2021

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Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, it is difficult to extract individual plants for mature rice plants with luxuriant branches and leaves, as well as bent and intersected panicles. Therefore, this paper proposes a clustering adaptive density estimation method based on the constructed LiDAR measurement system and double-threshold segmentation. The Otsu algorithm is adopted to construct a double-threshold according to elevation and inflection intensity in different parts of the rice plant, after reducing noise through the statistical outlier removal (SOR) algorithm. For adaptively parameter adjustment of supervoxel clustering and mean-shift clustering during density estimation, the calculation relationship between influencing factors (including seed-point size and kernel-bandwidth size) and number of points are, respectively, deduced by analysis. The experiment result of density estimation proved the two clustering methods effective, with a Root Mean Square Error (RMSE) of 9.968 and 5.877, and a Mean Absolute Percent Error (MAPE) of 5.67% and 3.37%, and the average accuracy was more than 90% and 95%, respectively. This estimation method is of positive significance for crop density measurement and could lay the foundation for intelligent harvest.

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Generating Time-Series Crop Surface Models from Data Acquired by a UAV-Based Laser Scanner System

Phan,

Huynh,

Takahashi

2023

Lecture Notes in Civil Engineering

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Method for estimating rice plant height without ground surface detection using laser scanner measurement

Cited by 15 publications

References 20 publications

Fundamental study for estimating rice-plant stem number using laser scanner measurements

Fundamental study for estimating rice-plant stem number using laser scanner measurements

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

Generating Time-Series Crop Surface Models from Data Acquired by a UAV-Based Laser Scanner System

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