H. Xiao, Yang Liu, Yande Liu, Hui Xiao, Liwei Sun, Yong Hao
{"title":"基于可见光谱与近红外光谱融合的柑橘绿色化诊断","authors":"H. Xiao, Yang Liu, Yande Liu, Hui Xiao, Liwei Sun, Yong Hao","doi":"10.3390/app131810082","DOIUrl":null,"url":null,"abstract":"A disease, known as citrus greening, is a major threat to the citrus industry. The objective of this study was to investigate the feasibility of rapid detection and improving the identification accuracy of citrus greening with visible and near-infrared spectra under spectral fusion. After we obtained the spectra of the collected citrus leaves and used the polymerase chain reaction for part of them, five types of samples were sorted out: slight, moderate, serious, nutrient deficiency, and normal. This study of spectral fusion was conducted on three levels as spectral data, characteristic, and model decision, and the identification capacity was tested using prediction samples. It was found that the effect of a least squares support vector machine model for feature-level fusion based on principal component analysis presented the best performance, while in the Lin_Kernel function; the accuracy was 100%, penalty coefficient γ was 0.09, and operation time was 0.66 s. It is better than the single spectral discriminant model. The results showed that the fusion of visible and near-infrared spectra was feasible for the nondestructive detection of citrus greening disease. This method is of great significance for the healthy development of the citrus industry, and provides important reference value for the application of spectral fusion in other fields.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnosis of Citrus Greening Based on the Fusion of Visible and Near-Infrared Spectra\",\"authors\":\"H. Xiao, Yang Liu, Yande Liu, Hui Xiao, Liwei Sun, Yong Hao\",\"doi\":\"10.3390/app131810082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A disease, known as citrus greening, is a major threat to the citrus industry. The objective of this study was to investigate the feasibility of rapid detection and improving the identification accuracy of citrus greening with visible and near-infrared spectra under spectral fusion. After we obtained the spectra of the collected citrus leaves and used the polymerase chain reaction for part of them, five types of samples were sorted out: slight, moderate, serious, nutrient deficiency, and normal. This study of spectral fusion was conducted on three levels as spectral data, characteristic, and model decision, and the identification capacity was tested using prediction samples. It was found that the effect of a least squares support vector machine model for feature-level fusion based on principal component analysis presented the best performance, while in the Lin_Kernel function; the accuracy was 100%, penalty coefficient γ was 0.09, and operation time was 0.66 s. It is better than the single spectral discriminant model. The results showed that the fusion of visible and near-infrared spectra was feasible for the nondestructive detection of citrus greening disease. This method is of great significance for the healthy development of the citrus industry, and provides important reference value for the application of spectral fusion in other fields.\",\"PeriodicalId\":48760,\"journal\":{\"name\":\"Applied Sciences-Basel\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Sciences-Basel\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.3390/app131810082\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Sciences-Basel","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3390/app131810082","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Diagnosis of Citrus Greening Based on the Fusion of Visible and Near-Infrared Spectra
A disease, known as citrus greening, is a major threat to the citrus industry. The objective of this study was to investigate the feasibility of rapid detection and improving the identification accuracy of citrus greening with visible and near-infrared spectra under spectral fusion. After we obtained the spectra of the collected citrus leaves and used the polymerase chain reaction for part of them, five types of samples were sorted out: slight, moderate, serious, nutrient deficiency, and normal. This study of spectral fusion was conducted on three levels as spectral data, characteristic, and model decision, and the identification capacity was tested using prediction samples. It was found that the effect of a least squares support vector machine model for feature-level fusion based on principal component analysis presented the best performance, while in the Lin_Kernel function; the accuracy was 100%, penalty coefficient γ was 0.09, and operation time was 0.66 s. It is better than the single spectral discriminant model. The results showed that the fusion of visible and near-infrared spectra was feasible for the nondestructive detection of citrus greening disease. This method is of great significance for the healthy development of the citrus industry, and provides important reference value for the application of spectral fusion in other fields.
期刊介绍:
Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.