Evidence map›Paper›PMID 39267072›Full record

ArticlePlant methods2024

GRABSEEDS: extraction of plant organ traits through image analysis.

Haibao Tang, Wenqian Kong, Pheonah Nabukalu, Johnathan S Lomas, Michel Moser, Jisen Zhang, Mengwei Jiang, Xingtan Zhang, Andrew H Paterson, Won Cheol Yim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant methods, 2024. 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.

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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

10 authors.

Haibao TangFujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology and College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China. tanghaibao@gmail.com.
Wenqian KongPlant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30605, USA.
Pheonah NabukaluThe Land Institute, 2440 E Water Well Road, Salina, KS, 67401, USA.
Johnathan S LomasDepartment of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, 89557, USA.
Michel MoserInstitute of Plant Sciences, University of Bern, Altenbergrain 21, 3013, Bern, Switzerland.
Jisen ZhangState Key Lab for Conservation and Utilization of Subtropical Agro-Biological Resources, Guangxi Key Lab for Sugarcane Biology, Guangxi University, Nanning, 530004, Guangxi, China.
Mengwei JiangNational Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, Guangdong, China.
Xingtan ZhangNational Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, Guangdong, China.
Andrew H PatersonPlant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30605, USA. paterson@uga.edu.
Won Cheol YimDepartment of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, 89557, USA. wyim@unr.edu.

Funding

National Key Research and Development Program of China 2021YFF1000101National Key Research and Development Program of China 2021YFF1000104
6 · The paper itself

Abstract

backgroundPhenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits.

resultsWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting.

conclusionGRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .

Indexed as

High throughputImage analysisPhenotypeQTL mappingSeed traits

Identifiers

PMID39267072
PMCPMC11397055

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Registered trials

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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.