Evidence map›Paper›PMID 36925753›Full record

ArticleFrontiers in plant science2023

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.

Shizhuang Weng, Junjie Ma, Wentao Tao, Yujian Tan, Meijing Pan, Zixi Zhang, Linsheng Huang, Ling Zheng, Jinling Zhao

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Article in Frontiers in plant science, 2023. 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

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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

1 citing paper in PubMed.

  1. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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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

9 authors.

Shizhuang WengNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Junjie MaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Wentao TaoNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Yujian TanNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Meijing PanNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Zixi ZhangNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Linsheng HuangNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Ling ZhengNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Jinling ZhaoNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Indexed as

drought stresshyperspectral imagingmulti-featuressubsample fusiontomato

Identifiers

PMID36925753
PMCPMC10011179

What Socratic holds

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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 Socratic graph.