Evidence map›Paper›PMID 37662154›Full record

ArticleFrontiers in plant science2023

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data.

Jiaxin Zhang, Aiwu Zhang, Zixuan Liu, Wanting He, Shengyuan Yang

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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 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Jiaxin ZhangKey Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.
Aiwu ZhangKey Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.
Zixuan LiuKey Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.
Wanting HeKey Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.
Shengyuan YangKey Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.

Indexed as

alfalfafuzzyhyperspectral imageLiDAR dataphenotypic traitsthe evaluation of salt tolerance

Identifiers

PMID37662154
PMCPMC10470838

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