Evidence map›Paper›PMID 41149299›Full record

ArticleBiosensors2025

Identification of Hybrid

Meng Zhang, Peng Li, Wei Dong, Shuqi Tang, Yan Wang, Runmei Li, Shucun Ju, Bolun Guan, Jingbo Zhu, Juanjuan Kong and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Meng ZhangAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.ORCID 0009-0001-0454-5054
Peng LiSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China.
Wei DongAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Shuqi TangCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Yan WangAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Runmei LiAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Shucun JuAnhui Disater Earnly Warning and Agricultural Meteorological Information Center, Hefei 230031, China.
Bolun GuanAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Jingbo ZhuAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Juanjuan KongAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Liping ZhangAgricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.

Funding

Agricultural Science and Technology Achievement Transformation Project of Anhui Academy of Agricultural Sciences 2025ZH032Central Leading Local Science and Technology Development Special Plan 202407a12020010Research Project of Anhui Academy of Agricultural Sciences 2025YL074Scientific Research Project of Fuyang Normal University 2024FSKJ31ZD
6 · The paper itself

Abstract

Paddy rice grain variety classification is essential for quality control, as different rice varieties exhibit significant variations in quality attributes, affecting both food security and market value. The integration of hyperspectral imaging with machine learning presents a promising approach for precise classification, though challenges remain in managing the high dimensionality and variability of spectral data, along with the need for model interpretability. To address these challenges, this study employs a CNN-Transformer model that incorporates Standard Normal Variate (SNV) preprocessing, Competitive Adaptive Reweighted Sampling (CARS) for feature wavelength selection, and interpretability analysis to optimize the classification of hybrid

Indexed as

Deep LearningHyperspectral ImagingOryzaEdible Grainhybrid indicahyperspectral imaginginterpretability analysismachine learningpaddy rice grainvariety classification

Identifiers

PMID41149299
PMCPMC12564839

What OpenQuestion holds

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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.