Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo
Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In...
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2025-01-01
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| author | Xingmei Xu Jiayuan Li Jing Zhou Puyu Feng Helong Yu Yuntao Ma |
| author_facet | Xingmei Xu Jiayuan Li Jing Zhou Puyu Feng Helong Yu Yuntao Ma |
| author_sort | Xingmei Xu |
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| description | Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (<i>Lentinula edodes</i>) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding <i>R</i><sup>2</sup> > 0.8 and <i>nRMSE</i> < 0.09 for the pileus transverse and longitudinal diameters, <i>R</i><sup>2</sup> = 0.53 and <i>RMSE</i> = 3.26 mm for the pileus height, <i>R</i><sup>2</sup> = 0.79 and <i>nRMSE</i> = 0.12 for stipe diameter, and <i>R</i><sup>2</sup> = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (<i>R</i><sup>2</sup> = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives. |
| format | Article |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Agriculture |
| spelling | doaj-art-e4ef88e4ade74cf5b0fb903e4d26140a2025-08-20T02:12:23ZengMDPI AGAgriculture2077-04722025-01-0115329810.3390/agriculture15030298Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View StereoXingmei Xu0Jiayuan Li1Jing Zhou2Puyu Feng3Helong Yu4Yuntao Ma5College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaPhenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (<i>Lentinula edodes</i>) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding <i>R</i><sup>2</sup> > 0.8 and <i>nRMSE</i> < 0.09 for the pileus transverse and longitudinal diameters, <i>R</i><sup>2</sup> = 0.53 and <i>RMSE</i> = 3.26 mm for the pileus height, <i>R</i><sup>2</sup> = 0.79 and <i>nRMSE</i> = 0.12 for stipe diameter, and <i>R</i><sup>2</sup> = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (<i>R</i><sup>2</sup> = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives.https://www.mdpi.com/2077-0472/15/3/298shiitake mushroomsstructure from motionpoint clouddeep learningpoint cloud semantic segmentation |
| spellingShingle | Xingmei Xu Jiayuan Li Jing Zhou Puyu Feng Helong Yu Yuntao Ma Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo Agriculture shiitake mushrooms structure from motion point cloud deep learning point cloud semantic segmentation |
| title | Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo |
| title_full | Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo |
| title_fullStr | Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo |
| title_full_unstemmed | Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo |
| title_short | Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo |
| title_sort | three dimensional reconstruction phenotypic traits extraction and yield estimation of shiitake mushrooms based on structure from motion and multi view stereo |
| topic | shiitake mushrooms structure from motion point cloud deep learning point cloud semantic segmentation |
| url | https://www.mdpi.com/2077-0472/15/3/298 |
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