Abstract
In order to study the method of identifying early hidden damage of kiwifruit, near infrared hyperspectral imaging system in the range of 900–1700 nm is used to acquire the near infrared hyperspectral imaging of sound kiwifruits and damage kiwifruits (in three hours). In this research, kernel-based partial least squares (KPLS) method is used to select the effective bands from 224 hyperspectral bands for reducing data dimension. Then principal component analysis (PCA) is applied to extract features from the effective bands. Finally, the classification result is obtained by the support vector machine (SVM), backpropagation neural network (BPNN) and extreme learning machine (ELM). In the experiment section, the proposed method with band selection based on kernel partial least square is compared with the method without band selection. For 69 sound kiwifruits and 69 invisible damaged kiwi fruits, a total of 138 samples were collected. The best accuracy of band selection based on KPLS method is 98.27%, which is obviously better than the result without band selection. The result shows that the near infrared hyperspectral imaging technique can be used to identify early hidden damage of kiwifruit, and the band selection method based on kernel partial least squares is very helpful to improve the recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hassankhani, R., Navid, H., Sayedarabi, H.: Potato surface defect detection in machine vision system. Afr. J. Agric. Res. 7(5), 844–850 (2012)
Throop, J.A., Aneshansley, D.J., Anger, W.C., Peterson, D.L.: Quality evaluation of apples based on surface defects: development of an automated inspection system. Postharvest Biol. Technol. 36(3), 281–290 (2005)
Kotwaliwale, N., Weckler, P.R., Brusewitz, G.H., Kranzler, G.A., Maness, N.O.: Non-destructive quality determination of pecans using soft X-rays. Postharvest Biol. Technol. 45(3), 372–380 (2007)
Moscetti, R., Haff, R.P., Monarca, D., Cecchini, M., Massantini, R.: Near-infrared spectroscopy for detection of hailstorm damage on olive fruit. Postharvest Biol. Technol. 120, 204–212 (2016)
Qiao, T., Ren, J., Craigie, C., Zabalza, J., Maltin, C., Marshall, S.: Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation. Comput. Electron. Agric. 115, 21–25 (2015)
Tschannerl, J., Ren, J., Jack, F., et al.: Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt. Food Chem. 270, 105–112 (2019)
Lü, Q., Tang, M.: Detection of hidden bruise on kiwi fruit using hyperspectral imaging and parallelepiped classification. Proc. Environ. Sci. 12(B), 1172–1179 (2012)
Li, J., Chen, L., Huang, W.: Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Postharvest Biol. Technol 135, 104–113 (2018)
Zabalza, J., et al.: Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185, 1–10 (2016)
Ren, J., Zabalza, J., Marshall, S., Zheng, J.: Effective feature extraction and data reduction in remote sensing using hyperspectral imaging [applications corner]. IEEE Signal Process. Mag. 31(4), 149–154 (2014)
Cen, H., Lu, R., Zhu, Q., Mendoza, F.: Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification. Postharvest Biol. Technol. 111, 352–361 (2016)
Wold, H.: Estimation of principal components and related models by iterative least squares. Multivariate Anal. 1, 391–420 (1966)
Nissen, L.R., Byrne, D.V., Bertelsen, G., Skibsted, L.H.: The antioxidative activity of plant extracts in cooked pork patties as evaluated by descriptive sensory profiling and chemical analysis. Meat Sci. 68(3), 485–495 (2004)
Geladi, P., Kowalski, B.R.: Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)
Sun, S., Peng, Q., Shakoor, A.: A kernel-based multivariate feature selection method for microarray data classification. PLoS One 9(7), e102541 (2014)
Farrell, M.D., Mersereau, R.M.: On the impact of PCA dimension reduction for hyperspectral detection of difficult targets. IEEE Geosci. Remote Sens. Lett. 2(2), 192–195 (2005)
Agarwal, A., El-Ghazawi, T., El-Askary, H., Le-Moigne, J.: Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 353–356 (2007)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol 2, 27:1–27:2 (2011)
Cao, F., et al.: Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 56(11), 1–17 (2018)
Zabalza, J., et al.: Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. IEEE Trans. Geosci. Remote Sens. 53(8), 4418–4433 (2015)
Zabalza, J., et al.: Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS J. Photogr. Remote Sens. 93, 112–122 (2014)
Qiao, T., Yang, Z., Ren, J., et al.: Joint bilateral filtering and spectral similarity-based sparse representation: a generic framework for effective feature extraction and data classification in hyperspectral imaging. Pattern Recogn. 77, 316–328 (2018)
Sun, H., Ren, J., Zhao, H., et al.: Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. Remote Sens. 11(5), 536 (2019)
Tschannerl, J., et al.: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm. Inform. Fusion 51, 189–200 (2019)
Qiao, T., et al.: Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions. J. Appl. Spectrosc. 82(1), 137–144 (2015)
Acknowledgement
This work is supported in part by the National Nature Science Foundation of China (nos. U1701266, 61471132), the Innovation Team Project of Guangdong Education Department (no. 2017KCXTD011), Natural Science Foundation of Guangdong Province China (no. 2018A030313751), and Science and Technology Program of Guangzhou, China (nos. 201803010065, 201802020010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y., Yang, Z., Cao, J., Ling, WK., Liu, Q. (2020). Detection of Invisible Damage of Kiwi Fruit Based on Hyperspectral Technique. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-39431-8_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39430-1
Online ISBN: 978-3-030-39431-8
eBook Packages: Computer ScienceComputer Science (R0)