Abstract
Classification, recognition, and authentication of wheat grain varieties are essential because their high purity results in high yield and quality guarantee. In the present study, sixteen (16) wheat varieties harvested from the Punjab region were chosen for classification. The images of the wheat seeds were captured from both sides using a near-infrared hyperspectral imaging system that covers all the spectral bands from 900–1700 nm wavelength. Two machine learning models, support vector machine (SVM) and linear discriminant analysis (LDA), were implemented to classify wheat varieties. The models were trained separately on raw spectral data and preprocessed spectral data. Three preprocessing techniques pretreated the mean spectra: standard normal variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky-Golay Smoothing (SG Smoothing) to abolish the interference caused by instrumental and environmental factors. The support vector machine obtained the best result on the raw spectral data with a test accuracy of 93%.
Supported by the Ministry of Human Resource Development (MHRD) INDIA with reference grant number: OH-3123200428.
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
Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans. Geosci. Remote Sens. 47(3), 862–873 (2009)
Bao, Y., Mi, C., Wu, N., Liu, F., He, Y.: Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics. Appl. Sci. 9(19), 4119 (2019)
Choudhary, R., Mahesh, S., Paliwal, J., Jayas, D.: Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosyst. Eng. 102(2), 115–127 (2009)
Fayyazi, S., Abbaspour-Fard, M., Rohani, A., Sadrnia, H., Monadjemi, S.A.H., et al.: Identification and classification of three Iranian rice seed varieties in mixed samples by morphological features using image processing and learning vector quantization neural network. Iran. Food Sci. Technol. Res. J. 10(3), 211–218 (2014)
Feng, L., Zhu, S., Liu, F., He, Y., Bao, Y., Zhang, C.: Hyperspectral imaging for seed quality and safety inspection: a review. Plant methods 15(1), 1–25 (2019)
Hadimani, L., Garg, N.M.: Automatic surface defects classification of kinnow mandarins using combination of multi-feature fusion techniques. J. Food Process. Eng. 44(1), e13589 (2021)
He, X., Feng, X., Sun, D., Liu, F., Bao, Y., He, Y.: Rapid and nondestructive measurement of rice seed vitality of different years using near-infrared hyperspectral imaging. Molecules 24(12), 2227 (2019)
Heisel, S.E., Peterson, D.M., Jones, B.: Identification of united states barley cultivars by sodium dodecyl sulfate polyacrylamide gel electrophoresis of hordeins. Cereal Chem. 63(6), 500–505 (1986)
Huang, M., He, C., Zhu, Q., Qin, J.: Maize seed variety classification using the integration of spectral and image features combined with feature transformation based on hyperspectral imaging. Appl. Sci. 6(6), 183 (2016)
Lim, J., et al.: Application of near infrared reflectance spectroscopy for rapid and non-destructive discrimination of hulled barley, naked barley, and wheat contaminated with fusarium. Sensors 18(1), 113 (2018)
Manickavasagan, A., Sathya, G., Jayas, D., White, N.: Wheat class identification using monochrome images. J. Cereal sci. 47(3), 518–527 (2008)
Manley, M., McGoverin, C.M., Engelbrecht, P., Geladi, P.: Influence of grain topography on near infrared hyperspectral images. Talanta 89, 223–230 (2012)
Mishra, P., Nordon, A., Tschannerl, J., Lian, G., Redfern, S., Marshall, S.: Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products. J. Food Eng. 238, 70–77 (2018)
Nie, P., Zhang, J., Feng, X., Yu, C., He, Y.: Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sens. Actuators B: Chem. 296, 126630 (2019)
Osae, R., Essilfie, G., Alolga, R.N., Bonah, E., Ma, H., Zhou, C.: Drying of ginger slices-evaluation of quality attributes, energy consumption, and kinetics study. J. Food Process. Eng. 43(2), e13348 (2020)
Qiu, Z., Chen, J., Zhao, Y., Zhu, S., He, Y., Zhang, C.: Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Appl. Sci. 8(2), 212 (2018)
Sahoo, P.K., Soltani, S., Wong, A.K.: A survey of thresholding techniques. Comput. Vis. Graph. Image process. 41(2), 233–260 (1988)
Sendin, K., Manley, M., Baeten, V., Fernández Pierna, J.A., Williams, P.J.: Near infrared hyperspectral imaging for white maize classification according to grading regulations. Food Anal. Methods 12(7), 1612–1624 (2019)
Sun, D.W.: Hyperspectral imaging for food quality analysis and control. Elsevier (2010)
Tujo, T., Kumar, D., Yitagesu, E., Girma, M.: A predictive model to predict seed classes using machine learning. Int. J. Eng. Tech. Res 6, 334–344 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tyagi, N., Raman, B., Garg, N.M. (2023). Varietal Classification of Wheat Seeds Using Hyperspectral Imaging Technique and Machine Learning Models. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)