Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point
High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex...
Saved in:
Main Authors: | Xuemei Liang, Wenbo Yu, Li Qin, Jianfeng Wang, Peng Jia, Qi Liu, Xiaoyu Lei, Minglai Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Agronomy |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4395/15/1/120 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution
by: Shizhao Li, et al.
Published: (2024-12-01) -
Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm
by: Lina Zhang, et al.
Published: (2025-01-01) -
A Method for Measuring Strawberry Leaf Area Based on Three-Dimensional Point Cloud Instance Segmentation
by: Zhipeng Li, et al.
Published: (2025-01-01) -
Automated Phenotypic Analysis of Mature Soybean Using Multi-View Stereo 3D Reconstruction and Point Cloud Segmentation
by: Daohan Cui, et al.
Published: (2025-01-01) -
LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11
by: Ameer Tamoor Khan, et al.
Published: (2025-01-01)