Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR
Yangyang Gu,
Hongxu Ai,
Tai Guo,
Peng Liu,
Yongqing Wang,
Hengbiao Zheng,
Tao Cheng,
Yan Zhu,
Weixing Cao,
Xia Yao
Affiliations
Yangyang Gu
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Hongxu Ai
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Tai Guo
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Peng Liu
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Yongqing Wang
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Hengbiao Zheng
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Tao Cheng
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Yan Zhu
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Weixing Cao
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
Xia Yao
National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
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/m2, 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/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, 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.
