Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation
Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available ph...
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MDPI AG
2024-11-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/13/23/3368 |
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| author | Xinghui Zhu Zhongrui Huang Bin Li |
| author_facet | Xinghui Zhu Zhongrui Huang Bin Li |
| author_sort | Xinghui Zhu |
| collection | DOAJ |
| description | Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R<sup>2</sup>) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants. |
| format | Article |
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| institution | DOAJ |
| issn | 2223-7747 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj-art-13ea63e2a839441d92658c60bb3d45be2025-08-20T02:50:40ZengMDPI AGPlants2223-77472024-11-011323336810.3390/plants13233368Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path SegmentationXinghui Zhu0Zhongrui Huang1Bin Li2College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaHunan Engineering Technology Research Center of Agricultural Rural Informatization, Changsha 410128, ChinaHunan Engineering Technology Research Center of Agricultural Rural Informatization, Changsha 410128, ChinaPrecise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R<sup>2</sup>) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants.https://www.mdpi.com/2223-7747/13/23/33683D reconstructionstem and leaf segmentationneural radiance fieldsstructure from motionpotted plant analysisplant digitization |
| spellingShingle | Xinghui Zhu Zhongrui Huang Bin Li Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation Plants 3D reconstruction stem and leaf segmentation neural radiance fields structure from motion potted plant analysis plant digitization |
| title | Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation |
| title_full | Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation |
| title_fullStr | Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation |
| title_full_unstemmed | Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation |
| title_short | Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation |
| title_sort | three dimensional phenotyping pipeline of potted plants based on neural radiation fields and path segmentation |
| topic | 3D reconstruction stem and leaf segmentation neural radiance fields structure from motion potted plant analysis plant digitization |
| url | https://www.mdpi.com/2223-7747/13/23/3368 |
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