Evidence map›Paper›PMID 41618555›Full record

ReviewPlant communications2026

Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.

Ningyuan Yang, Yong He, Mostafa Gouda, Zhenyu Huang, Runzhou Cao, Yongqiang Shi, Yuefeng Chen, Yu Tang, Yong Yang, Zhangjian Hu and 2 more

Abstract readReview
In one paragraph

Review in Plant communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ningyuan YangCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Yong HeCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Mostafa GoudaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza 12622, Egypt.
Zhenyu HuangCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Runzhou CaoCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Yongqiang ShiSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Yuefeng ChenZhejiang Branch of Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Shaoxing 312039, China.
Yu TangAcademy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Yong YangState Key Laboratory for Quality and Safety of Agro-products, Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Zhangjian HuCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Cheng PengState Key Laboratory for Quality and Safety of Agro-products, Key Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China. Electronic address: pc_phm@163.com.
Xuping FengCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. Electronic address: fengxp@zju.edu.cn.

Funding

Non-US Government Research Support type
6 · The paper itself

Abstract

With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively entering an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques-leveraging multidimensional spatial resolution capabilities-have overcome the limitations of two-dimensional (2D) phenotyping in breeding analyses, enabling precise characterization of crop spatial interactions, spatial distribution of plant architecture, and complex 3D structural traits. Recent breakthroughs in computer technology for 3D reconstruction and 3D segmentation have provided robust technical support for crop 3D phenotypic analysis. Furthermore, effective integration of extracted 3D phenotypic data with genotypic data serves as a powerful tool for future research on crop gene function and genomics-assisted breeding. This review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies. In parallel, we describe the latest interdisciplinary advances in 3D phenotyping within crop functional genomics research and genomics-assisted breeding. We objectively evaluate the advantages and limitations of 3D phenotyping compared with 2D approaches to assist breeders in selecting appropriate technologies. Finally, we propose future perspectives to promote deeper integration of phenomics and breeding technologies. Despite existing conceptual and technical challenges, it is foreseeable that cross-disciplinary integration of phenomics and genomics will offer promising prospects for crop breeding.

Indexed as

Crops, AgriculturalGenomicsImaging, Three-DimensionalPlant BreedingArtificial IntelligencePhenotypeartificial intelligencecrop 3D phenotypingcrop 3D reconstructiondeep learninggenomics-assisted breeding

Identifiers

PMID41618555
PMCPMC12983262

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.