Evidence map›Paper›PMID 41374667›Full record

ArticleSensors (Basel, Switzerland)2025

Investigating the Association Between Citrus Huanglongbing and Chlorophyll Content Using Hyperspectral Detection.

Shiqing Dou, Minglan Li, Xiangqian Qi, Yichang Hou, Shixin Yuan, Yaqin Song, Zhengmin Mei, Genhong Qi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

8 authors.

Shiqing DouCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Minglan LiCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.ORCID 0009-0000-3128-7495
Xiangqian QiSchool of Resource Engineering, Longyan University, Fujian 364012, China.ORCID 0009-0002-5187-5512
Yichang HouCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Shixin YuanCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Yaqin SongGuangxi Key Laboratory of Germplasm Innovation and Utilization of Specialty Commercial Crops in North Guangxi, Guangxi Citrus Breeding and Cultivation Technology Innovation Center, Guangxi Academy of Specialty Crops, Guilin 541004, China.
Zhengmin MeiGuangxi Key Laboratory of Germplasm Innovation and Utilization of Specialty Commercial Crops in North Guangxi, Guangxi Citrus Breeding and Cultivation Technology Innovation Center, Guangxi Academy of Specialty Crops, Guilin 541004, China.
Genhong QiCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.

Funding

the Guilin City Science Research and Technology Development Plan Project No. 20230120-13the Key Research and Development Program Project of Guangxi No. 2025FNFN97094the National Natural Science Foundation of China No.42561059the Natural Science Foundation of Fujian Province No.2023J01957the Natural Science Foundation of Guangxi No. 2023JJA150097
6 · The paper itself

Abstract

Huanglongbing (HLB) poses a severe threat to the sustainable citrus industry, causing significant alterations in the spectral reflectance and leaf chlorophyll content (LCC) of citrus leaves. This study investigates the quantitative relationship between spectral characteristics and LCC for the early detection of HLB in Mianju mandarin cultivars. We analyzed hyperspectral data from healthy and HLB-infected leaves, employing the least absolute shrinkage and selection operator (LASSO) method and spectral indices to select chlorophyll characteristic bands, and several machine learning models were used to estimate the LCC. The results indicate that: (1) HLB-infected leaves exhibit significantly different spectral reflectance, characterized by a distinct "blueshift of the red edge"; (2) a greater proportion of characteristic bands for HLB-infected leaves were located in the near-infrared region compared to healthy leaves; and (3) the LASSO-PLSR model demonstrated high predictive accuracy for LCC estimation-for healthy leaves (Rv

Indexed as

ChlorophyllCitrusHyperspectral ImagingPlant DiseasesMachine LearningPlant LeavesChlorophyllfeature selectionHuanglongbingleaf chlorophyll contentmachine learningspectral reflectance

Identifiers

PMID41374667
PMCPMC12694103

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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.