Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR.

Abstract

Background The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. Results The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m 2 , rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m 2 , rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m 2 , rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. Conclusions The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

Plant phenotyping relevance

TLSと点群アルゴリズムを用いたコムギ穂数の非破壊推定法を開発・比較検証しており、植物形質取得が研究の中心である。

Code and data availability

The supplied article blocks describe TLS point cloud data and two ear-counting algorithms (DBSC and VBRG), but contain no data availability statement, no public repository deposit of point clouds or manual counts, and no author code/scripts or model checkpoints with a public URL. The only URLs present are the RIEGL仪器厂商

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