Diagnosis of Citrus Greening Based on the Fusion of Visible and Near-Infrared Spectra

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY Applied Sciences-Basel Pub Date : 2023-09-07 DOI:10.3390/app131810082
H. Xiao, Yang Liu, Yande Liu, Hui Xiao, Liwei Sun, Yong Hao
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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.
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基于可见光谱与近红外光谱融合的柑橘绿色化诊断
一种被称为柑橘绿化的疾病是对柑橘产业的主要威胁。本研究的目的是研究在光谱融合的情况下,利用可见光和近红外光谱快速检测和提高柑橘绿化鉴定准确性的可行性。在我们获得收集到的柑橘叶片的光谱并使用聚合酶链式反应对其中的一部分进行检测后,我们将其分为五类:轻度、中度、严重、营养缺乏和正常。本文从光谱数据、特征和模型决策三个层面对光谱融合进行了研究,并使用预测样本测试了识别能力。研究发现,基于主成分分析的最小二乘支持向量机模型在特征级融合中的效果最好,而在Lin_ Kernel函数中;准确率为100%,惩罚系数γ为0.09,操作时间为0.66s,优于单谱判别模型。结果表明,可见光谱与近红外光谱的融合方法可用于柑橘绿化病害的无损检测。该方法对柑橘产业的健康发展具有重要意义,对光谱融合在其他领域的应用具有重要参考价值。
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
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.
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