Evidence map›Paper›PMID 36522641›Full record

ArticlePlant methods2022

CropPainter: an effective and precise tool for trait-to-image crop visualization based on generative adversarial networks.

Lingfeng Duan, Zhihao Wang, Hongfei Chen, Jinyang Fu, Hanzhi Wei, Zedong Geng, Wanneng Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant methods, 2022. 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

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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
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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

7 authors.

Lingfeng Duan *National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Zhihao Wang *National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Hongfei ChenNational Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Jinyang FuNational Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Hanzhi WeiNational Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Zedong GengNational Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Wanneng YangNational Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. ywn@mail.hzau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVirtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information.

resultsThis study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online.

conclusionIn conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.

Indexed as

Crop phenotypic traitsCrop visualizationDeep learningGenerative adversarial networkVirtual plant

Identifiers

PMID36522641
PMCPMC9753368

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LicenceCC BY
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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.