Evidence map›Paper›PMID 35408374›Full record

ArticleSensors (Basel, Switzerland)2022

Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology.

Shuheng Zhang, Hanguo Zeng, Wei Ji, Kun Yi, Shuangfeng Yang, Peisheng Mao, Zhanjun Wang, Hongqian Yu, Manli Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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.

Shuheng ZhangCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Hanguo ZengCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Wei JiCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Kun YiCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Shuangfeng YangCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Peisheng MaoCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.ORCID 0000-0003-3261-0949
Zhanjun WangInstitute of Desertification Control, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China.
Hongqian YuInstitute of Desertification Control, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China.
Manli LiCollege of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.ORCID 0000-0002-0018-1265

Funding

Reproduction and industrialization demonstration of high-quality forage seeds 2020 - Pro - Sci - Tech in Inner Mongolia, China - Technology Innovation Center of Forage Seed Industry - 3the China Agriculture Research System of MOF and MARA Peisheng Mao
6 · The paper itself

Abstract

Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.

Indexed as

GerminationMedicago sativaDiscriminant AnalysisSeedsTechnologymultispectral imagingmultivariate analysisseed germinationseed viabilityseed vigor

Identifiers

PMID35408374
PMCPMC9003024

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.