Evidence map›Paper›PMID 42038787›Full record

ArticlePlant phenomics (Washington, D.C.)2026

PlantSpecLab: A comprehensive open-source platform for high-throughput plant spectral data processing and phenotypic modeling.

Ruoyu Di, Pan Gao, Chengkai Li, Shiwei Ruan, Fei Tan, Weiping Yan, Zhihao Liang, Jingye Liu, Chu Zhang, Wei Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ruoyu DiCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Pan GaoCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Chengkai LiCollege of Agriculture, Shihezi University, Shihezi, 832003, China.
Shiwei RuanSchool of Computing and Information Technology, Great Bay University, Dongguan, 523000, China.
Fei TanCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Weiping YanCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Zhihao LiangCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Jingye LiuCollege of Information Science and Technology, Shihezi University, Shihezi, 832003, China.
Chu ZhangSchool of Information Engineering, Huzhou University, Huzhou, 313000, China.
Wei XuCollege of Agriculture, Shihezi University, Shihezi, 832003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding a mean classification accuracy of 82.86 % for tomato maturity and average

Indexed as

Fractional-order differencingHyperspectral imagingImage segmentationOpen-source softwarePlant phenotyping

Identifiers

PMID42038787
PMCPMC13109321

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.