Anterior|Siguiente

Variety identification model for maize seeds using hyperspectral pixel-level information combined with convolutional neural network

  • role： First author第一作者
  • Affiliation：

    Institute of Information and Communication, Harbin Engineering University, Harbin 150000, China

  • Email：wangliguo@hrbeu.edu.cn
  • Introduction：王立国，1974年生，男，教授，博士生导师，研究方向为遥感图像处理。E-mail： wangliguo@hrbeu.edu.cn
WANG Liguo,  
  • Affiliation：

    Institute of Information and Communication, Harbin Engineering University, Harbin 150000, China

WANG Lifeng

resumen

As an important grain crop in China, maize has many varieties and is prone to misclassification, affecting agricultural security and food production. With the development of hyperspectral imaging and deep learning technology, identifying crop varieties using a combination of both is possible. A Convolutional Neural Network (CNN), the most representative algorithm of deep learning technology to deal with the image classification task, needs a large number of training samples in the model training process. However, obtaining a large number of hyperspectral images of maize seed samples is difficult and time consuming. Aiming at the problem of the large number of modeling samples required for the traditional method based on CNN for crop identification in hyperspectral images, a variety identification model for maize seeds based on hyperspectral pixel-level spectral information and CNN is proposed. First, hyperspectral images of different varieties of maize seeds in the range of 400—1000 nm are obtained, and 203-dimensional spectral information of all the pixels of samples is extracted. Nevertheless, the enormous amount of spectral information creates the problem of dimensional disaster and greatly increases the computational cost. Second, to reduce the dimensionality of the sample spectral information, the principal component analysis algorithm is used to reduce the spectral dimension to eight dimensions, which effectively shortens the operation time. Third, the pixel-level spectral information of the sample (i.e., the spectral information of all the pixels of the sample) is applied to the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification models, in addition to the CNN model. The experimental results demonstrate that for the CNN, SVM, and KNN recognition algorithms, the pixel-level spectral information models show a more stable and efficient recognition effect than the seed-level one (i.e., the average of all pixel spectral information of each sample). The seed-level information model does not fully utilize the sample pixel spectrum and spatial information, which needs a large number of modeling samples. When the number of samples used to build the classification model is the same, the CNN model has a significantly better recognition effect than the SVM and KNN models. In accordance with all the pixel-level classification results, a majority voting strategy is used to identify corn seed sample variety, and the sample recognition accuracy is up to 100% (100% refers to the identification accuracy when the numbers of samples in the modeling and test sets are 0.27 and 0.32, respectively. As the number of samples in the test set increases, the identification accuracy decreases). Lastly, the t-distribution random neighbor embedding algorithm is used to realize the visualization of output eigenvalues of CNN. The features of different maize seed varieties are clearly bounded in the visualization, which adequately verifies the validity of the species recognition model based on hyperspectral pixel-level information and CNN. In the rare case of modeling seed samples, nondestructive and efficient variety identification of maize seeds is realized, which will provide a theoretical basis for precision agriculture.

palabra clave

hyperspectral image;convolutional neural network;deep learning;maize seed;t-distribution random neighbor embedding algorithm;pixel-level spectral information

References

  1. 1.
    Alishahi A, Farahmand H, Prieto N and Cozzolino D. 2010. Identification of transgenic foods using NIR spectroscopy: a review. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 75(1): 1-7
  2. 2.
    Cao X L, Deng M J and Cui G X. 2019. Identifying ramie variety by combining the hyperspectral technology with the principal component analysis. Spectroscopy and Spectral Analysis, 39(6): 1905-1908
  3. 3.
    Jia S Q. 2015. Study on the Purity Identification of Maize Seed Based on Near Infrared Imaging. Beijing: China Agricultural University: 1-5
  4. 4.
    Jolliffe I T. 1986. Principal Component Analysis. New York: Springer-Verlag: 111-130
  5. 5.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444
  6. 6.
    Li Q J，Liu J，Mi X F，Yang J and Yu T. 2021. Object-oriented crop classification for GF-6 WFV remote sensing images based on Convolutional Neural Network. National Remote Sensing Bulletin, 25(2): 549-558
  7. 7.
    Maxwell A E, Warner T A and Fang F. 2018. Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9): 2784-2817
  8. 8.
    Nie P C, Zhang J N, Feng X P, Yu C L and He Y. 2019. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sensors and Actuators B: Chemical, 296: 126630
  9. 9.
    Paoletti M E, Haut J M, Plaza J and Plaza A. 2018. A new deep convolutional neural network for fast hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 120-147
  10. 10.
    Qiu Z J, Chen J, Zhao Y Y, Zhu S S, He Y and Zhang C. 2018. Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Applied Sciences, 8(2): 212
  11. 11.
    Rodríguez-Pulido F J, Barbin D F, Sun D W, Gordillo B, González-Miret M L and Heredia F J. 2013. Grape seed characterization by NIR hyperspectral imaging. Postharvest Biology and Technology, 76: 74-82
  12. 12.
    Scholkopf B and Smola A J. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge: MIT Press: 187-404
  13. 13.
    Serranti S, Cesare D, Marini F and Bonifazi G. 2013. Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta, 103: 276-284
  14. 14.
    Shrestha S, Knapič M, Žibrat U, Deleuran L C and Gislum R. 2016. Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis. Sensors and Actuators B: Chemical, 237: 1027-1034
  15. 15.
    Van Der Maaten L and Hinton G. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, 9: 2579-2605
  16. 16.
    Wang L, Sun D W, Pu H B and Zhu Z W. 2016. Application of hyperspectral imaging to discriminate the variety of maize seeds. Food Analytical Methods, 9(1): 225-234
  17. 17.
    Wang L G and Du X P. 2017. Semi-supervised classification of hyperspectral images applying the combination of K-mean clustering and twin support vector machine. Applied Science and Technology, 44(3): 12-18
  18. 18.
    Wang L G, Zhao L and Liu D F. 2018. A review on the application of SVM in hyperspectral image processing. Journal of Harbin Engineering University, 39(6): 973-983
  19. 19.
    Wei L F. 2017. Research on Detection Method of Maize Variety Based on Hyperspectral Image. Shenyang: Shenyang Agricultural University: 2-5
  20. 20.
    Wu X, Zhang W Z, Lu J F, Qiu Z J and He Y. 2016. Study on visual identification of corn seeds based on hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 36(2): 511-514
  21. 21.
    Yang J X, Zhao Y Q and Chan J C W. 2017. Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4729-4742
  22. 22.
    Zhang H K, Li Y and Jiang Y N. 2018. Deep learning for hyperspectral imagery classification: the state of the art and prospects. Acta Automatica Sinica, 44(6): 961-977
  23. 23.
    Zhao W D，Li S S，Li A，Zhang B and Chen J. 2021. Deep fusion of hyperspectral images and multi-source remote sensing data for classification with convolutional neural network. National Remote Sensing Bulletin，25（7）：1489- 1502
  24. 24.
    Zhao Y Y, Zhu S S, Zhang C, Feng X P, Feng L and He Y. 2018. Application of hyperspectral imaging and chemometrics for variety classification of maize seeds. RSC Advances, 8(3): 1337-1345

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website