Abstract
Ensuring the identification and authenticity of wheat seeds are critical tasks in the food grain industry. In this work, twenty wheat varieties were collected from three different locations in India. The near-infrared (NIR) hyperspectral imaging technique (spectral range 900–1700 nm) was employed in conjunction with machine learning models to discriminate twenty different wheat varieties into two classes: hard wheat and soft wheat. The data images were taken from both sides of the seed (ventral and dorsal side). The dataset includes images of 20,160 seeds. Five different machine learning models were used for classification: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). The models were trained using the mean spectral values extracted from the hyperspectral images. Five preprocessing techniques pretreated the mean spectral values of the hyperspectral image: Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky Golay Smoothing (SG), Savitzky Golay First Derivative (SG-1), and Savitzky Golay Second Derivative (SG-2). The model’s performance was evaluated for both raw and preprocessed data. The Support Vector Machine exhibited exceptional performance, attaining an astonishing accuracy rate of 95.01% for amalgamated data (encompassing both ventral and dorsal side data), 95.05% for exclusively ventral side data, and an impressive 95.37% for exclusively dorsal side data.
Supported by the Ministry of Education (MoE) 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
Abu Alfeilat, H.A., et al.: Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big Data 7(4), 221–248 (2019)
Allahverdiyev, T.I., Talai, J.M., Huseynova, I.M., Aliyev, J.A.: Effect of drought stress on some physiological parameters, yield, yield components of durum (Triticum durum desf.) and bread (Triticum aestivum L.) wheat genotypes. Ekin J. Crop Breed. Genet. 1(1), 50–62 (2015)
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)
Berrar, D.: Bayes’ theorem and Naive Bayes classifier. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, vol. 403, p. 412 (2018)
Choudhary, R., Mahesh, S., Paliwal, J., Jayas, D.: Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosys. Eng. 102(2), 115–127 (2009)
Fabiyi, S.D., et al.: Comparative study of PCA and LDA for rice seeds quality inspection. In: 2019 IEEE AFRICON, pp. 1–4. IEEE (2019)
Feng, L., Zhu, S., Liu, F., He, Y., Bao, Y., Zhang, C.: Hyperspectral imaging for seed quality and safety inspection: a review. Plant Meth. 15(1), 1–25 (2019)
Hacini, N., Djelloul, R., Hadef, A., Samson, M.F., Desclaux, D.: Comparative characterization of grain protein content and composition by chromatography-based separation methods (SE-HPLC and RP-HPLC) of ten wheat varieties grown in different agro-ecological zones of Algeria. Separations 9(12), 443 (2022)
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5(2), 1 (2015)
Issarny, C., Cao, W., Falk, D., Seetharaman, K., Bock, J.E.: Exploring functionality of hard and soft wheat flour blends for improved end-use quality prediction. Cereal Chem. 94(4), 723–732 (2017)
Katyal, M., Singh, N., Chopra, N., Kaur, A.: Hard, medium-hard and extraordinarily soft wheat varieties: comparison and relationship between various starch properties. Int. J. Biol. Macromol. 123, 1143–1149 (2019)
Khatri, A., Agrawal, S., Chatterjee, J.M.: Wheat seed classification: utilizing ensemble machine learning approach. Sci. Program. 2022, 1–9 (2022)
Kirk, P.L.: Kjeldahl method for total nitrogen. Anal. Chem. 22(2), 354–358 (1950)
Lozano-Sánchez, J., Borrás-Linares, I., Sass-Kiss, A., Segura-Carretero, A.: Chromatographic technique: high-performance liquid chromatography (HPLC). In: Modern Techniques for Food Authentication, pp. 459–526. Elsevier (2018)
Lu, Y., Saeys, W., Kim, M., Peng, Y., Lu, R.: Hyperspectral imaging technology for quality and safety evaluation of horticultural products: a review and celebration of the past 20-year progress. Postharvest Biol. Technol. 170, 111318 (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)
Sabanci, K., Kayabasi, A., Toktas, A.: Computer vision-based method for classification of wheat grains using artificial neural network. J. Sci. Food Agric. 97(8), 2588–2593 (2017)
Sharma, A., Singh, T., Garg, N.: Combining near-infrared hyperspectral imaging and ANN for varietal classification of wheat seeds. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), pp. 1103–1108. IEEE (2022)
Singh, T., Garg, N.M., Iyengar, S.R.: Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network. J. Food Process Eng. 44(10), e13821 (2021)
Sricharoonratana, M., Thompson, A.K., Teerachaichayut, S.: Use of near infrared hyperspectral imaging as a nondestructive method of determining and classifying shelf life of cakes. LWT 136, 110369 (2021)
Tyagi, N., Raman, B., Garg, N.M.: 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, pp. 253–266. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-31417-9_20
Unlersen, M.F., et al.: CNN-SVM hybrid model for varietal classification of wheat based on bulk samples. Eur. Food Res. Technol. 248(8), 2043–2052 (2022)
Zhang, L., et al.: Identification of seed maize fields with high spatial resolution and multiple spectral remote sensing using random forest classifier. Remote Sens. 12(3), 362 (2020)
Acknowledgement
The research work received support from the Ministry of Education (MoE), INDIA under the reference grant number OH-3123200428. Furthermore, one of the authors, Balasubramanian Raman, expressed gratitude for the financial assistance provided by the SERB MATRICS project under file no. MTR/2022/000187.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tyagi, N., Raman, B., Garg, N. (2024). Classification of Hard and Soft Wheat Species Using Hyperspectral Imaging and Machine Learning Models. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_43
Download citation
DOI: https://doi.org/10.1007/978-981-99-8181-6_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8180-9
Online ISBN: 978-981-99-8181-6
eBook Packages: Computer ScienceComputer Science (R0)