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Agrobacterium-Mediated Virus-Induced Gene Silencing Assay In Cotton

Agrobacterium-Mediated Virus-Induced Gene Silencing Assay In Cotton

Published on: August 20, 2011

DCSFormer: a high-precision method for cotton seedling point cloud organ segmentation.

Tengfei Liu¹, Weili Sun¹, Haoyu Jiang¹

¹College of Smart Intelligence (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing, China.

Frontiers in Plant Science

|February 9, 2026

Summary

DCSFormer accurately segments cotton seedling organs from 3D point clouds, overcoming challenges in morphology and data quality. This method significantly improves high-throughput plant phenotyping and digital breeding applications.

Keywords:

CLFSkip DCS Block DCSFormer cotton point cloud organ segmentation

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Area of Science:

Agricultural Engineering
Computer Vision
Plant Science

Background:

Accurate segmentation of cotton seedling organs from 3D point clouds is crucial for high-throughput plant phenotyping and digital breeding.
Challenges include complex organ morphology, noisy data, and limited annotated datasets.

Purpose of the Study:

To develop an advanced deep learning model for precise cotton seedling organ segmentation from 3D point clouds.
To address limitations of existing methods in handling complex morphology and data noise.

Main Methods:

Proposing DCSFormer, an extension of Point Transformer V3, featuring a DCS Block for adaptive feature extraction and CLFSkip for multi-scale feature fusion.
Constructing a novel annotated cotton seedling dataset for model training and validation.

Main Results:

DCSFormer achieved high performance metrics: 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1.
Outperformed comparison models on cotton seedling data and demonstrated superior generalizability on public datasets (Crops3D, Pheno4D).

Conclusions:

DCSFormer offers an effective solution for precise cotton seedling organ segmentation.
The model's advancements in feature extraction and fusion enhance performance in plant phenotyping and digital breeding.