Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud
Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing me...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/10/2409 |
| _version_ | 1797476099728343040 |
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| author | Fangzheng Hu Chengda Lin Junwen Peng Jing Wang Ruifang Zhai |
| author_facet | Fangzheng Hu Chengda Lin Junwen Peng Jing Wang Ruifang Zhai |
| author_sort | Fangzheng Hu |
| collection | DOAJ |
| description | Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>²</mo></mrow></semantics></math></inline-formula> of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples <i>t</i>-test with significant correlation (<i>p</i> < 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information. |
| first_indexed | 2024-03-09T20:53:09Z |
| format | Article |
| id | doaj.art-096a68dad8194f67b39a4881c562bbef |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:53:09Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-096a68dad8194f67b39a4881c562bbef2023-11-23T22:26:39ZengMDPI AGAgronomy2073-43952022-10-011210240910.3390/agronomy12102409Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point CloudFangzheng Hu0Chengda Lin1Junwen Peng2Jing Wang3Ruifang Zhai4College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaExploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>²</mo></mrow></semantics></math></inline-formula> of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples <i>t</i>-test with significant correlation (<i>p</i> < 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information.https://www.mdpi.com/2073-4395/12/10/2409agricultural robotsmart agricultureleaf area estimationhybrid filtersurface reconstruction |
| spellingShingle | Fangzheng Hu Chengda Lin Junwen Peng Jing Wang Ruifang Zhai Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud Agronomy agricultural robot smart agriculture leaf area estimation hybrid filter surface reconstruction |
| title | Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud |
| title_full | Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud |
| title_fullStr | Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud |
| title_full_unstemmed | Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud |
| title_short | Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud |
| title_sort | rapeseed leaf estimation methods at field scale by using terrestrial lidar point cloud |
| topic | agricultural robot smart agriculture leaf area estimation hybrid filter surface reconstruction |
| url | https://www.mdpi.com/2073-4395/12/10/2409 |
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