Evidence map›Paper›PMID 41821316›Full record

ReviewPlant communications2026

Integrating AI in seed science: Toward an intelligent design paradigm.

Ying Zhang, Jianjun Du, Guanmin Huang, Yue Zhao, Peng Man, Anran Song, Yanxin Zhao, Qingmei Men, Chuanyu Wang, Minkun Guo and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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.

Ying ZhangInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China; Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding, Beijing 100097, China.
Jianjun DuInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China; Yazhouwan National Laboratory, Sanya, Hainan 572000, China.
Guanmin HuangInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Yue ZhaoInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Peng ManInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Anran SongInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Yanxin ZhaoBeijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Qingmei MenInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Chuanyu WangInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Minkun GuoInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China.
Xinyu GuoInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China. Electronic address: guoxy73@163.com.
Chunjiang ZhaoInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; Beijing Key Laboratory of Digital Plant, Beijing 100097, China. Electronic address: zhaocj@nercita.org.cn.

Funding

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

Abstract

Global agricultural systems face mounting threats to food security from climate change, population growth, and land degradation, with current productivity gains insufficient to meet the demands of a projected global population of 9.7 billion by 2050. Seeds, as both carriers of genetic information and the foundation of agricultural production, directly determine crop yield, resilience, and quality. Advancing seed innovation is therefore essential for achieving sustainable increases in agricultural productivity. This review traces the evolution of seed science from agrarian civilization to the era of intelligent seed design and summarizes recent advances in AI-based methodological innovations and applications. We introduce the emerging paradigm of AI-driven seed design, outline its core scientific questions and key technologies, and propose integrated technological pathways. Furthermore, we analyze current challenges and highlight future directions in this field. By integrating the latest research and technological developments, this review aims to establish an "AI for Science" paradigm for future-oriented seed research that meets the increasing global demand for sustainable and high-quality seed resources.

Indexed as

AgricultureArtificial IntelligenceCrops, AgriculturalSeedsartificial intelligencedigital twinintelligent agentmulti-omicsseed phenotyping

Identifiers

PMID41821316
PMCPMC13174215

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.