Skip to main content
SpringerLink
Log in

Ensemble deep learning for high-precision classification of 90 rice seed varieties from hyperspectral images

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

To develop rice varieties with better nutritional qualities, it is important to classify rice seeds accurately. Hyperspectral imaging can be used to extract spectral information from rice seeds, which can then be used to classify them into different varieties. The challenges of precise classification increase when there are many classes and few training samples. In this paper, we present a novel method for high-precision Hyperspectral Image (HSI) classification of 90 different classes of rice seeds using ensemble deep learning. Our method first employs band selection techniques to select the optimal hyperspectral bands for rice seed classification. Then, a deep neural network is trained with the selected hyperspectral and RGB data from rice seed images to obtain different models for different bands. Finally, an ensemble of deep learning models is employed to classify rice seed images and improve classification accuracy. The proposed method achieves an overall precision ranging from 92.73 to 96.17% despite a large number of classes and low data samples for each class and with only 15 selected hyperspectral bands. This precision is significantly higher than the state-of-the-art classical machine learning methods like random forest, confirming the effectiveness of the proposed method in classifying hyperspectral images of rice seeds.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Availability of data and materials

Data will be available on reasonable request.

References

  • Archibald R, Fann G (2007) Feature selection and classification of hyperspectral images with support vector machines. IEEE Geosci Remote Sens Lett 4:674–677

    Article  Google Scholar 

  • Bao Y, Mi C, Wu N et al (2019) Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics. Appl Sci 9:4119

    Article  Google Scholar 

  • Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data

  • Camoglu G, Demirel K, Genc L (2018) Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper. Quant Infrared Thermogr J 15:81–94

    Article  Google Scholar 

  • Chang C-I (2003) Hyperspectral imaging: techniques for spectral detection and classification. Springer Science & Business Media

    Book  Google Scholar 

  • Chang C-I, Plaza A (2006) A fast iterative algorithm for implementation of pixel purity index. IEEE Geosci Remote Sens Lett 3:63–67

    Article  Google Scholar 

  • Chen S-Y, Chiu M-F, Zou X-W (2022) Real-time defect inspection of green coffee beans using NIR snapshot hyperspectral imaging. Comput Electron Agric 197:106970

    Article  Google Scholar 

  • Du Q, Yang H (2008) Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci Remote Sens Lett 5:564–568

    Article  Google Scholar 

  • Du Q, Raksuntorn N, Younan NH, King RL (2008) End-member extraction for hyperspectral image analysis. Appl Opt 47:F77–F84

    Article  Google Scholar 

  • Du H, Qi H, Wang X et al (2003) Band selection using independent component analysis for hyperspectral image processing. In: 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings. IEEE, pp 93–98

  • Fabiyi SD, Vu H, Tachtatzis C et al (2020) Varietal classification of rice seeds using RGB and hyperspectral images. IEEE Access 8:22493–22505

    Article  Google Scholar 

  • Fei B (2019) Hyperspectral imaging in medical applications. In: Data Handling in Science and Technology. Elsevier, pp 523–565

  • Feng S, Itoh Y, Parente M, Duarte MF (2017) Hyperspectral band selection from statistical wavelet models. IEEE Trans Geosci Remote Sens 55:2111–2123

    Article  Google Scholar 

  • Feng L, Zhu S, Liu F et al (2019) Hyperspectral imaging for seed quality and safety inspection: a review. Plant Methods 15:1–25

    Article  Google Scholar 

  • Fox N, Parbhakar-Fox A, Moltzen J et al (2017) Applications of hyperspectral mineralogy for geoenvironmental characterisation. Miner Eng 107:63–77

    Article  Google Scholar 

  • Ganaie MA, Hu M, Malik AK et al (2022) Ensemble deep learning: a review. Eng Appl Artif Intell 115:105151

    Article  Google Scholar 

  • Gianchandani N, Jaiswal A, Singh D et al (2023) Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images. J Ambient Intell Human Comput 14:5541–5553. https://doi.org/10.1007/s12652-020-02669-6

    Article  Google Scholar 

  • Głomb P, Romaszewski M, Cholewa M, Domino K (2018) Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns. Forensic Sci Int 290:227–237

    Article  Google Scholar 

  • Halicek M, Fabelo H, Ortega S et al (2019) In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers (basel) 11:756

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778. https://doi.org/10.5281/zenodo.3241923

  • Hu N, Li W, Du C et al (2021) Predicting micronutrients of wheat using hyperspectral imaging. Food Chem 343:128473

    Article  Google Scholar 

  • Jin B, Qi H, Jia L et al (2022) Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning. Infrared Phys Technol 122:104097

    Article  Google Scholar 

  • Khodor M, Makki I, Younes R et al (2021) Landmine detection in hyperspectral images based on pixel intensity. Remote Sens Appl Soc Environ 21:100468

    Google Scholar 

  • Kumar B, Dikshit O, Gupta A, Singh MK (2020) Feature extraction for hyperspectral image classification: a review. Int J Remote Sens 41:6248–6287

    Article  Google Scholar 

  • Lazcano R, Madroñal D, Salvador R et al (2017) Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture. J Syst Archit 77:101–111

    Article  Google Scholar 

  • Lim S, Sohn KH, Lee C (2001) Principal component analysis for compression of hyperspectral images. In: IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217). IEEE, pp 97–99

  • Liu Y, Pu H, Sun D-W (2017) Hyperspectral imaging technique for evaluating food quality and safety during various processes: a review of recent applications. Trends Food Sci Technol 69:25–35

    Article  Google Scholar 

  • Liu Z, Lu Y, Peng Y et al (2019) Estimation of soil heavy metal content using hyperspectral data. Remote Sens 11:1464

    Article  Google Scholar 

  • Lu G, Fei B (2014) Medical hyperspectral imaging: a review. J Biomed Opt 19:10901

    Article  Google Scholar 

  • Mukundan A, Huang C-C, Men T-C et al (2022) Air pollution detection using a novel snap-shot hyperspectral imaging technique. Sensors 22:6231

    Article  Google Scholar 

  • Nagasubramanian K, Jones S, Singh AK et al (2019) Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15:1–10

    Article  Google Scholar 

  • Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48:2297–2307

    Article  Google Scholar 

  • Park B, Lu R (2015) Hyperspectral imaging technology in food and agriculture. Springer

    Book  Google Scholar 

  • Peng Y, Fan M, Song J et al (2018) Assessment of plant species diversity based on hyperspectral indices at a fine scale. Sci Rep 8:1–11

    Article  Google Scholar 

  • Peng J, Sun W, Li H-C et al (2021) Low-rank and sparse representation for hyperspectral image processing: a review. IEEE Geosci Remote Sens Mag 10:10–43

    Article  Google Scholar 

  • Peyghambari S, Zhang Y (2021) Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. J Appl Remote Sens 15:31501

    Article  Google Scholar 

  • Prabhakar M, Prasad YG, Rao MN (2012) Remote sensing of biotic stress in crop plants and its applications for pest management. Crop Stress its Manag Perspect Strateg 517–545

  • Qu J, Du Q, Li Y et al (2020) Anomaly detection in hyperspectral imagery based on Gaussian mixture model. IEEE Trans Geosci Remote Sens 59:9504–9517

    Article  Google Scholar 

  • Ran Q, Liu Z, Sun X et al (2021) Anomaly detection for hyperspectral images based on improved low-rank and sparse representation and joint Gaussian mixture distribution. IEEE J Sel Top Appl Earth Obs Remote Sens 14:6339–6352

    Article  Google Scholar 

  • Ren H, Chang C-I (2003) Automatic spectral target recognition in hyperspectral imagery. IEEE Trans Aerosp Electron Syst 39:1232–1249

    Article  Google Scholar 

  • Sethy PK, Pandey C, Sahu YK, Behera SK (2022) Hyperspectral imagery applications for precision agriculture-a systemic survey. Multimed Tools Appl 1–34

  • Shimoni M, Haelterman R, Perneel C (2019) Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques. IEEE Geosci Remote Sens Mag 7:101–117

    Article  Google Scholar 

  • Shinde S, Patidar H (2022) Hyperspectral image classification using principle component analysis and deep convolutional neural network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s11042-021-11729-8

    Article  Google Scholar 

  • Singh CB, Jayas DS, Paliwal J, White NDG (2009) Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. J Stored Prod Res 45:151–158

    Article  Google Scholar 

  • Sun W, Du Q (2019) Hyperspectral band selection: A review. IEEE Geosci Remote Sens Mag 7:118–139

    Article  Google Scholar 

  • Sun J, Zhang L, Zhou X et al (2021) A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology. J Food Process Eng 44:e13797

    Article  Google Scholar 

  • Taheri AM, Mahdavi-Nasab H (2018) Sparse representation based facial image compression via multiple dictionaries and separated ROI. Multimed Tools Appl 77:31095–31114

    Article  Google Scholar 

  • Taheri A, Ebrahimnezhad H, Sedaaghi MH (2022) Prediction of the critical temperature of superconducting materials using image regression and ensemble deep learning. Mater Today Commun 33:104743

    Article  Google Scholar 

  • Uddin MP, Al MM, Hossain MA (2021) PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech Rev 38:377–396

    Article  Google Scholar 

  • Velesaca HO, Suárez PL, Mira R, Sappa AD (2021) Computer vision based food grain classification: a comprehensive survey. Comput Electron Agric 187:106287

    Article  Google Scholar 

  • Veraverbeke S, Dennison P, Gitas I et al (2018) Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sens Environ 216:105–121

    Article  Google Scholar 

  • Vigneau N, Ecarnot M, Rabatel G, Roumet P (2011) Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. F Crop Res 122:25–31

    Article  Google Scholar 

  • Wang Y, Song S (2023) Variety identification of sweet maize seeds based on hyperspectral imaging combined with deep learning. Infrared Phys Technol 130:104611

    Article  Google Scholar 

  • Wang F, Gao J, Zha Y (2018) Hyperspectral sensing of heavy metals in soil and vegetation: feasibility and challenges. ISPRS J Photogramm Remote Sens 136:73–84

    Article  Google Scholar 

  • Wang C, Liu B, Liu L et al (2021) A review of deep learning used in the hyperspectral image analysis for agriculture. Artif Intell Rev 54:5205–5253

    Article  Google Scholar 

  • Weng S, Tang P, Yuan H et al (2020) Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion. Spectrochim Acta Part A Mol Biomol Spectrosc 234:118237

    Article  Google Scholar 

  • Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—part I: fundamentals. Innov Food Sci Emerg Technol 19:1–14

    Article  Google Scholar 

  • Yang X, Ye Y, Li X et al (2018) Hyperspectral image classification with deep learning models. IEEE Trans Geosci Remote Sens 56:5408–5423

    Article  Google Scholar 

  • Yao H, Huang Y, Tang L, et al (2018) Using hyperspectral data in precision farming applications. In: Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation. CRC Press, pp 3–35

  • Yoon J (2022) Hyperspectral Imaging for Clinical Applications. BioChip J 16(1):1–12

    Article  Google Scholar 

  • Zhang X, Sun Y, Shang K et al (2016) Crop classification based on feature band set construction and object-oriented approach using hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 9:4117–4128

    Article  Google Scholar 

  • Zhang J, Dai L, Cheng F (2021) Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network. J Food Meas Charact 15:484–494

    Article  Google Scholar 

  • Zhang G, Xu T, Tian Y et al (2022) Classification of rice leaf blast severity using hyperspectral imaging. Sci Rep 12:19757

    Article  Google Scholar 

  • Zhao X, Que H, Sun X et al (2022) Hybrid convolutional network based on hyperspectral imaging for wheat seed varieties classification. Infrared Phys Technol 125:104270

    Article  Google Scholar 

  • Zheng Z, Liu Y, He M et al (2022) Effective band selection of hyperspectral image by an attention mechanism-based convolutional network. RSC Adv 12:8750–8759

    Article  Google Scholar 

  • Zhou L, Zhang C, Taha MF et al (2020) Wheat kernel variety identification based on a large near-infrared spectral dataset and a novel deep learning-based feature selection method. Front Plant Sci 11:575810

    Article  Google Scholar 

  • Zhu S, Zhou L, Gao P et al (2019) Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties. Molecules 24:3268

    Article  Google Scholar 

Download references

Funding

There is no funding.

Author information

Authors and Affiliations

  1. Computer Vision Research Laboratory, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

    AmirMasoud Taheri, Hossein Ebrahimnezhad & Mohammadhossein Sedaaghi

Authors
  1. AmirMasoud Taheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Ebrahimnezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammadhossein Sedaaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Ebrahimnezhad.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taheri, A., Ebrahimnezhad, H. & Sedaaghi, M. Ensemble deep learning for high-precision classification of 90 rice seed varieties from hyperspectral images. J Ambient Intell Human Comput 15, 2883–2899 (2024). https://doi.org/10.1007/s12652-024-04782-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-024-04782-2

Keywords

Search

Navigation