Hyperspectral reflectance imaging combined with carbohydrate metabolism analysis for diagnosis of citrus Huanglongbing in different seasons and cultivars

Weng, Haiyong; Lv, Jingwen; Cen, Haiyan; He, Mubin; Zeng, Yibing; Hua, Shijia; Li, Hongye; Meng, Youqing; Fang, Hui; He, Yong

doi:10.1016/j.snb.2018.08.020

Cited by 48 publications

(28 citation statements)

References 43 publications

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“…It was necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra were processed using ten preprocessing methods, including multiplicative scatter correction (MSC) [ 23 ], standardized normal variate (SNV) [ 24 ], normalization [ 25 ], autoscales [ 26 ], mean centering (MC) [ 27 ], moving average method (MA) [ 28 ], detrend fluctuation analysis (Detrend) [ 29 ], Savitsky–Golay smoothing (SG) [ 30 ], Savitsky–Golay first derivative (SG-FD) [ 31 ], and Savitsky–Golay second derivative (SG-SD) [ 32 ]. To reduce calculation and increase calculation speed, competitive adaptive reweighted sampling (CARS) [ 33 ], principal components analysis (PCA) [ 34 ], and successive projections algorithm (SPA) [ 35 ] are preferable to extract feature wavelengths to reduce the dimensionality.…”

Section: Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission, and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00%, and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence, i.e., the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and for selecting the incident angle of a light source, and they have potential applications for online non-destructive testing.

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Section: Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Dai

Jiang

Lü

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It is necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra are processed by ten pre-processing methods, including Multiplicative Scatter Correction (MSC) [18], Standardized Normal Variate (SNV) [19], Normalization [20], Autoscales [21], Mean Centering (MC) [22], Moving-Average Method (MA) [23], Detrend Fluctuation Analysis (Detrend) [24], Savitsky-Golay Smoothing (SG) [25], Savitsky-Golay-First Derivative (SG-FD) [26] and Savitsky-Golay-Second Derivative (SG-SD) [27]. to distinguish due to their obvious spoilage and unpleasant smell deterioration, so they will not be discussed in this article.…”

Section: Spectrum Processing Methodsmentioning

confidence: 99%

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Dai¹,

Jiang²,

Lu³

et al. 2020

Preprint

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Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00% and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence due that the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and selecting the incident angle of the light source, and have potential applications in online non-destructive testing.

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“…The mean spectrum of all pixels within each ROI in the range of 450-1023 nm was calculated. In order to further remove the undesirable noise that could impact the extraction of spectral features, and eventually improve the discrimination accuracy, the Savitsky-Golay (SG) smoothing algorithm was also implemented as previously described [10]. were optimized with the aim of achieving the best output performance.…”

Section: Hyperspectral Images Acquisition and Processingmentioning

confidence: 99%

“…Currently, optical techniques, especially hyperspectral imaging (HSI) has been widely used for plant diseases detection [6][7][8] due to the advantage of providing spectral and spatial information simultaneously which contain information of chemical compositions and physical structures of the plant. Several studies have reported that HSI was feasible for plant disease detection, such as Plasmopara viticola in grape [9], green diseases in citrus [10], Magnaporthe oryzae in rice [11], gray mold in tomato [12,13]. As for SSR detection, one study conducted by Zhang et al on SSR detection of oilseed rape leaves using mid-infrared spectrum provided an over 80% detection accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

et al. 2019

Self Cite

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Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.

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Hyperspectral reflectance imaging combined with carbohydrate metabolism analysis for diagnosis of citrus Huanglongbing in different seasons and cultivars

Cited by 48 publications

References 43 publications

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

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