Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages
Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of ri...
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/15/1579 |
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| author | Md Rejaul Karim Md Nasim Reza Shahriar Ahmed Kyu-Ho Lee Joonjea Sung Sun-Ok Chung |
| author_facet | Md Rejaul Karim Md Nasim Reza Shahriar Ahmed Kyu-Ho Lee Joonjea Sung Sun-Ok Chung |
| author_sort | Md Rejaul Karim |
| collection | DOAJ |
| description | Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m<sup>3</sup>, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m<sup>3</sup>, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m<sup>3</sup>, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r<sup>2</sup> values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. |
| format | Article |
| id | doaj-art-ea27fc228c124449a8ed3f70b6468f4f |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Agriculture |
| spelling | doaj-art-ea27fc228c124449a8ed3f70b6468f4f2025-08-20T03:36:30ZengMDPI AGAgriculture2077-04722025-07-011515157910.3390/agriculture15151579Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing StagesMd Rejaul Karim0Md Nasim Reza1Shahriar Ahmed2Kyu-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 KoreaPrecise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m<sup>3</sup>, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m<sup>3</sup>, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m<sup>3</sup>, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r<sup>2</sup> values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability.https://www.mdpi.com/2077-0472/15/15/1579precision agriculturericeplant geometry3D point cloudcanopy structure |
| spellingShingle | Md Rejaul Karim Md Nasim Reza Shahriar Ahmed Kyu-Ho Lee Joonjea Sung Sun-Ok Chung Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages Agriculture precision agriculture rice plant geometry 3D point cloud canopy structure |
| title | Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages |
| title_full | Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages |
| title_fullStr | Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages |
| title_full_unstemmed | Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages |
| title_short | Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages |
| title_sort | proximal lidar sensing for monitoring of vegetative growth in rice at different growing stages |
| topic | precision agriculture rice plant geometry 3D point cloud canopy structure |
| url | https://www.mdpi.com/2077-0472/15/15/1579 |
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