ArticleSensors (Basel, Switzerland)2018
Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.Plant phenomics (Washington, D.C.) · 2025Article
- Automated measurement of field crop phenotypic traits using UAV 3D point clouds and an improved PointNet+.Frontiers in plant science · 2025Article
- KAN-GLNet: An enhanced PointNet++ model for canola silique segmentation and counting.PloS one · 2025Article
- Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation.Plants (Basel, Switzerland) · 2024Article
- Automatic Measurement of Seed Geometric Parameters Using a Handheld Scanner.Sensors (Basel, Switzerland) · 2024Article
- LT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments.Frontiers in plant science · 2024Article
- An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.Frontiers in plant science · 2024Article
- Automatic Measurement of Morphological Traits of Typical Leaf Samples.Sensors (Basel, Switzerland) · 2021Article
- Deep Segmentation of Point Clouds of Wheat.Frontiers in plant science · 2021Article
- Three-Dimensional Monitoring of Plant Structural Parameters and Chlorophyll Distribution.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.