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Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning.

Liyi Luo¹, Xintong Jiang¹, Yu Yang^1,2

¹Bioresource Engineering Department, McGill University, Montreal, QC, Canada.

Plant Phenomics (Washington, D.C.)

|August 4, 2023

Summary

This summary is machine-generated.

This study introduces Eff-3DPSeg, a weakly supervised deep learning method for 3D plant shoot segmentation. It significantly reduces annotation effort while achieving high accuracy in extracting plant traits, aiding plant breeding.

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Area of Science:

Computer Vision
Plant Science
Machine Learning

Background:

Accurate 3D plant shoot segmentation is crucial for extracting organ-level phenotypic traits.
Fully supervised deep learning demands extensive, time-consuming point-wise annotations.
Developing efficient annotation strategies is vital for advancing plant phenotyping.

Purpose of the Study:

To propose a novel weakly supervised framework, Eff-3DPSeg, for automated 3D plant shoot segmentation.
To reduce the annotation burden in deep learning models for plant organ segmentation.
To enable accurate extraction of plant phenotypic traits for improved plant breeding.

Main Methods:

Reconstructed high-resolution 3D point clouds of soybean using low-cost photogrammetry.
Developed a Meshlab-based Plant Annotator for efficient point cloud annotation.
Implemented a weakly supervised deep learning approach involving self-supervised pretraining and minimal fine-tuning (0.5% annotated points).

Main Results:

Eff-3DPSeg achieved high precision (95.1%), recall (96.6%), F1 score (95.8%), and mIoU (92.2%) for soybean stem-leaf segmentation.
Demonstrated comparable performance to fully supervised methods with significantly reduced annotation effort.
Successfully extracted key phenotypic traits including stem diameter, leaf width, and leaf length.

Conclusions:

Weakly supervised deep learning offers an effective solution for 3D plant shoot segmentation, minimizing annotation costs.
The Eff-3DPSeg framework provides a scalable approach for characterizing 3D plant architecture.
This method has the potential to accelerate plant breeding by enhancing selection processes through detailed phenotyping.