Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears

Fang, Yiming; Yang, Fan; Zhou, Zhu; Lin, Lujun; Li, Xiaoqin

doi:10.1155/2019/6715247

Cited by 22 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tere are multiple correlations between R, A, and K-M spectra at diferent wavelengths, resulting in redundant information in the spectra. Tis redundant information reduces the discrimination speed and accuracy of the model, and therefore, the redundant information needs to be eliminated [15]. In practice, a good performing classifcation model requires not only high discrimination accuracy but also has fast discrimination speed; therefore, the original spectra can be downscaled by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) [16].…”

Section: Characteristic Wavelength Selectionmentioning

confidence: 99%

Identification of Damage in Pear Using Hyperspectral Imaging Technology

Yin

et al. 2022

Journal of Spectroscopy

View full text Add to dashboard Cite

Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (A) spectra and Kubelka–Munk (K-M) spectra were added to reflectance (R) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on R, A, and K-M spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered R, A, and K-M. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on a spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral.

show abstract

Section: Characteristic Wavelength Selectionmentioning

confidence: 99%

Identification of Damage in Pear Using Hyperspectral Imaging Technology

Yin

et al. 2022

Journal of Spectroscopy

View full text Add to dashboard Cite

show abstract

“…However, due to the selection of feature wavelengths from a single perspective, this can result in an incomplete understanding of sample data, reducing the sensitivity of the method. Additionally, for certain analysis tasks, single-wavelength selection methods may misjudge feature wavelengths, thereby affecting the accuracy and stability of the results [24][25] .…”

Section: Introductionmentioning

confidence: 99%

Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

Zeng,

Huang,

Fang

et al. 2024

2024 International Conference on Optoelectronic Information and Optical Engineering (OIOE 2024)

View full text Add to dashboard Cite

By using wavelength selection methods to eliminate irrelevant and redundant information from near-infrared spectroscopy data before modeling, the performance of the apple sugar degree prediction model can be improved. However, there are many shortcomings in commonly used single-wavelength selection methods. This paper combines the competitive reweighted sampling (CARS) algorithm, the successive projection algorithm (SPA), and the genetic algorithm (GA), which are three primary single-wavelength selection methods, to merge the wavelength subsets obtained from them. These subsets are then combined through intersection and union fusion, resulting in 8 Venetian wavelength subsets. A fructose prediction model for apples is established by combining them with the Ridge Regression model. The results show that the number of wavelength subsets obtained by the preferred CARS∪SPA method is 74, accounting for 14.4% of the total wavelengths. Its R2p is maximum at 0.952, and the minimum RMSEp is 0.036. This indicates that the wavelength selection method of CARS∪SPA reduces the multicollinearity of spectral data, effectively utilizes complementary information in the wavelength variables obtained by CARS and SPA methods, and enhances the robustness and accuracy of the model. The research work can provide theoretical basis for the rapid grading of apple quality and the development of related detection devices.

show abstract

“…It holds the greatest potential for achieving practical applications in online detection, surpassing other technologies in this regard. 7 Currently, hyperspectral imaging technology has been introduced into fruit postharvest quality and safety assessment, such as defect detection on citrus, [24][25][26][27] apple, [28][29][30][31][32] pear, 33,34 peach, 14 cherry, 35 strawberry 36 and blueberry. 16 Hyperspectral imaging technology is a powerful tool for non-destructive detection of fruit surface defects.…”

Section: Introductionmentioning

confidence: 99%

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm

Tian¹,

Liu

He³

et al. 2023

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUNDBruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non‐destructive characteristics, hyperspectral imaging technology is a potential tool for non‐destructive detection of fruit surface defects.RESULTSIn the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo‐color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo‐color image of followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two‐band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively.CONCLUSIONThe bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.

show abstract

Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears

Cited by 22 publications

References 33 publications

Identification of Damage in Pear Using Hyperspectral Imaging Technology

Identification of Damage in Pear Using Hyperspectral Imaging Technology

Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm

Contact Info

Product

Resources

About