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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-11-01
|
| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-023-01093-z |
| _version_ | 1797452524414828544 |
|---|---|
| author | Yangyang Gu Hongxu Ai Tai Guo Peng Liu Yongqing Wang Hengbiao Zheng Tao Cheng Yan Zhu Weixing Cao Xia Yao |
| author_facet | Yangyang Gu Hongxu Ai Tai Guo Peng Liu Yongqing Wang Hengbiao Zheng Tao Cheng Yan Zhu Weixing Cao Xia Yao |
| author_sort | Yangyang Gu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:09:57Z |
| format | Article |
| id | doaj.art-90f2f634b7c74b41b3f25755be98b280 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-03-09T15:09:57Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-90f2f634b7c74b41b3f25755be98b2802023-11-26T13:26:53ZengBMCPlant Methods1746-48112023-11-0119111810.1186/s13007-023-01093-zComparison of two novel methods for counting wheat ears in the field with terrestrial LiDARYangyang Gu0Hongxu Ai1Tai Guo2Peng Liu3Yongqing Wang4Hengbiao Zheng5Tao Cheng6Yan Zhu7Weixing Cao8Xia Yao9National 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityNational 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 UniversityAbstract 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.https://doi.org/10.1186/s13007-023-01093-zEar numberLiDARDensity-based spatial clustering based on the normal (DBSC)Voxel-based regional growth (VBRG) |
| spellingShingle | Yangyang Gu Hongxu Ai Tai Guo Peng Liu Yongqing Wang Hengbiao Zheng Tao Cheng Yan Zhu Weixing Cao Xia Yao Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR Plant Methods Ear number LiDAR Density-based spatial clustering based on the normal (DBSC) Voxel-based regional growth (VBRG) |
| title | Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR |
| title_full | Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR |
| title_fullStr | Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR |
| title_full_unstemmed | Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR |
| title_short | Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR |
| title_sort | comparison of two novel methods for counting wheat ears in the field with terrestrial lidar |
| topic | Ear number LiDAR Density-based spatial clustering based on the normal (DBSC) Voxel-based regional growth (VBRG) |
| url | https://doi.org/10.1186/s13007-023-01093-z |
| work_keys_str_mv | AT yangyanggu comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT hongxuai comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT taiguo comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT pengliu comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT yongqingwang comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT hengbiaozheng comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT taocheng comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT yanzhu comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT weixingcao comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar AT xiayao comparisonoftwonovelmethodsforcountingwheatearsinthefieldwithterrestriallidar |