An overlapping-free leaf segmentation method for plant point clouds

by   Dawei Li, et al.

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97 for Epipremnum aureum, 99 and 87 reaches an average Recall at 100.00 F-measure at 99.66 automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.


page 4

page 6

page 7

page 14

page 17

page 18

page 19

page 22


Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants

In this work, the use of t-SNE is proposed to embed 3D point clouds of p...

Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds

Reliable and automated 3D plant shoot segmentation is a core prerequisit...

Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering

A common requirement of plant breeding programs across the country is co...

Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud

Nowadays, there are many approaches to acquire three-dimensional (3D) po...

Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

Segmentation of structural parts of 3D models of plants is an important ...

Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping

Semantic labeling of 3D point clouds is important for the derivation of ...

Please sign up or login with your details

Forgot password? Click here to reset