Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data
The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spa...
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MDPI AG
2024-12-01
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| Series: | Journal of Imaging |
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| author | Md Rejaul Karim Shahriar Ahmed Md Nasim Reza Kyu-Ho Lee Joonjea Sung Sun-Ok Chung |
| author_facet | Md Rejaul Karim Shahriar Ahmed Md Nasim Reza Kyu-Ho Lee Joonjea Sung Sun-Ok Chung |
| author_sort | Md Rejaul Karim |
| collection | DOAJ |
| description | The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r<sup>2</sup>) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m<sup>3</sup> and 14.09 ± 2.10 m<sup>3</sup>, respectively, with an MAE of 0.57 m<sup>3</sup>, an RMSE of 0.61 m<sup>3</sup>, an r<sup>2</sup> value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r<sup>2</sup> values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards. |
| format | Article |
| id | doaj-art-4b7307ce63e7478a982f920f250b14b6 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Journal of Imaging |
| spelling | doaj-art-4b7307ce63e7478a982f920f250b14b62025-01-24T13:36:14ZengMDPI AGJournal of Imaging2313-433X2024-12-01111510.3390/jimaging11010005Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud DataMd Rejaul Karim0Shahriar Ahmed1Md Nasim Reza2Kyu-Ho Lee3Joonjea Sung4Sun-Ok Chung5Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaFYD Company Ltd., Suwon 16676, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaThe geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r<sup>2</sup>) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m<sup>3</sup> and 14.09 ± 2.10 m<sup>3</sup>, respectively, with an MAE of 0.57 m<sup>3</sup>, an RMSE of 0.61 m<sup>3</sup>, an r<sup>2</sup> value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r<sup>2</sup> values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards.https://www.mdpi.com/2313-433X/11/1/5smart agricultureLiDAR sensorpoint cloud datatree canopy volumetree recognition |
| spellingShingle | Md Rejaul Karim Shahriar Ahmed Md Nasim Reza Kyu-Ho Lee Joonjea Sung Sun-Ok Chung Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data Journal of Imaging smart agriculture LiDAR sensor point cloud data tree canopy volume tree recognition |
| title | Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data |
| title_full | Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data |
| title_fullStr | Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data |
| title_full_unstemmed | Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data |
| title_short | Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data |
| title_sort | geometric feature characterization of apple trees from 3d lidar point cloud data |
| topic | smart agriculture LiDAR sensor point cloud data tree canopy volume tree recognition |
| url | https://www.mdpi.com/2313-433X/11/1/5 |
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