Three-Dimensional Reconstruction, Phenotypic Traits Extraction, and Yield Estimation of Shiitake Mushrooms Based on Structure from Motion and Multi-View Stereo

Xingmei Xu, Jiayuan Li, Jing Zhou, Puyu Feng, Helong Yu () and Yuntao Ma ()
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Xingmei Xu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jiayuan Li: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jing Zhou: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Puyu Feng: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Helong Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yuntao Ma: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 3, 1-20

Abstract: 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 ( Lentinula edodes ) 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 R 2 > 0.8 and nRMSE < 0.09 for the pileus transverse and longitudinal diameters, R 2 = 0.53 and RMSE = 3.26 mm for the pileus height, R 2 = 0.79 and nRMSE = 0.12 for stipe diameter, and R 2 = 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 ( R 2 = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives.

Keywords: shiitake mushrooms; structure from motion; point cloud; deep learning; point cloud semantic segmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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