Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms
The leaf area index (LAI) is a critical parameter for characterizing plant foliage abundance, canopy structure changes, and vegetation productivity in ecosystems. Traditional phenological measurements are often destructive, time-consuming, and labor-intensive. This paper proposes a high-throughput 3...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1501612/full |
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| author | Bing Shi Luqi Guo Lejun Yu |
| author_facet | Bing Shi Luqi Guo Lejun Yu |
| author_sort | Bing Shi |
| collection | DOAJ |
| description | The leaf area index (LAI) is a critical parameter for characterizing plant foliage abundance, canopy structure changes, and vegetation productivity in ecosystems. Traditional phenological measurements are often destructive, time-consuming, and labor-intensive. This paper proposes a high-throughput 3D point cloud data processing pipeline to segment field soybean plants and estimate their LAI. The 3D point cloud data is obtained from a UAV equipped with a LiDAR camera. First, The PointNet++ model was applied to simplify the segmentation process by isolating field soybean plants from their surroundings and eliminating environmental complexities. Subsequently, individual segmentation was achieved using the Watershed approach and k-means clustering algorithms, segmenting the field soybeans into individual plants. Finally, the LAI of soybean plant was estimated using a machine learning method and validated against measured values. The PointNet++ model improved segmentation accuracy by 6.73%, and the watershed algorithm achieved F1 scores of 0.89–0.90, outperforming k-means in complex adhesion cases. For LAI estimation, the SVM model showed the highest accuracy (R² = 0.79, RMSE = 0.47), with RF and XGBoost also performing well (R² > 0.69, RMSE< 0.65). This indicates that the individual segmentation algorithm, Watershed-based approach combined with PointNet++, can serve as a crucial foundation for extracting high-throughput plant phenotypic data. The experimental results confirm that the proposed method can rapidly calculate the morphological parameters of each soybean plant, making it suitable for high-throughput soybean phenotyping. |
| format | Article |
| id | doaj-art-39188babdb0d4f9bae4611410d1a5068 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-39188babdb0d4f9bae4611410d1a50682025-01-22T07:11:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15016121501612Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithmsBing ShiLuqi GuoLejun YuThe leaf area index (LAI) is a critical parameter for characterizing plant foliage abundance, canopy structure changes, and vegetation productivity in ecosystems. Traditional phenological measurements are often destructive, time-consuming, and labor-intensive. This paper proposes a high-throughput 3D point cloud data processing pipeline to segment field soybean plants and estimate their LAI. The 3D point cloud data is obtained from a UAV equipped with a LiDAR camera. First, The PointNet++ model was applied to simplify the segmentation process by isolating field soybean plants from their surroundings and eliminating environmental complexities. Subsequently, individual segmentation was achieved using the Watershed approach and k-means clustering algorithms, segmenting the field soybeans into individual plants. Finally, the LAI of soybean plant was estimated using a machine learning method and validated against measured values. The PointNet++ model improved segmentation accuracy by 6.73%, and the watershed algorithm achieved F1 scores of 0.89–0.90, outperforming k-means in complex adhesion cases. For LAI estimation, the SVM model showed the highest accuracy (R² = 0.79, RMSE = 0.47), with RF and XGBoost also performing well (R² > 0.69, RMSE< 0.65). This indicates that the individual segmentation algorithm, Watershed-based approach combined with PointNet++, can serve as a crucial foundation for extracting high-throughput plant phenotypic data. The experimental results confirm that the proposed method can rapidly calculate the morphological parameters of each soybean plant, making it suitable for high-throughput soybean phenotyping.https://www.frontiersin.org/articles/10.3389/fpls.2024.1501612/fullUAVLiDARhigh-throughputsoybeanmachine learningPointNet++ |
| spellingShingle | Bing Shi Luqi Guo Lejun Yu Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms Frontiers in Plant Science UAV LiDAR high-throughput soybean machine learning PointNet++ |
| title | Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms |
| title_full | Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms |
| title_fullStr | Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms |
| title_full_unstemmed | Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms |
| title_short | Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms |
| title_sort | accurate lai estimation of soybean plants in the field using deep learning and clustering algorithms |
| topic | UAV LiDAR high-throughput soybean machine learning PointNet++ |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1501612/full |
| work_keys_str_mv | AT bingshi accuratelaiestimationofsoybeanplantsinthefieldusingdeeplearningandclusteringalgorithms AT luqiguo accuratelaiestimationofsoybeanplantsinthefieldusingdeeplearningandclusteringalgorithms AT lejunyu accuratelaiestimationofsoybeanplantsinthefieldusingdeeplearningandclusteringalgorithms |