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Identification of haploid and diploid maize seeds using hybrid transformer model

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Abstract

Increasingly, more effective breeding techniques for new variations are preferred due to population growth and climatic change, particularly the accurate identification of the target variety. Maize haploid breeding technology, which can shorten the reproductive period and improve germplasm, has become the key to new maize breeding. In this study, a method in which deep features and image patches are analyzed together was proposed using a dataset consisting of 3000 different haploid/diploid type maize seed images in total. To achieve this objective, we adopted convolutional neural networks (CNNs) to recognize haploid and diploid maize seeds automatically through a transfer learning approach. More specifically, DenseNet201, ResNet152, ResNetRS50, RegNetX002, EfficientNetV2B0, EfficientB0, EfficientB1, EfficientB2, EfficientB3, EfficientB4, EfficientB5, EfficientB6, and EfficientB7 were applied for this specific task. The proposed hybrid model is inspired by both transfer learning and vision transformers. The error, accuracy, f1-score, recall, precision, and AUC of hybrid proposed model were 0.1491, 0.9633, and 0.9712, respectively. The accuracy rate reached, and the proposed model requires less processing in terms of complexity, which reveals the need for further investigation of such hybrid models. On the other hand, with the results obtained, it has been revealed that the maize seeds can be separated as haploid and diploid with traditional methods can be done much faster and without the need for an expert decision.

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Data availability

Data can be found in this link (http://rovile.org/datasets/haploid-and-diploid-maize-seeds-dataset/).

References

  1. Haque, M.A., et al.: Deep learning-based approach for identification of diseases of maize crop. Sci. Rep. 12(1), 1 (2022). https://doi.org/10.1038/s41598-022-10140-z

    Article  Google Scholar 

  2. Xu, P., Yang, R., Zeng, T., Zhang, J., Zhang, Y., Tan, Q.: Varietal classification of maize seeds using computer vision and machine learning techniques. J. Food Process Eng. 44(11), e13846 (2021). https://doi.org/10.1111/jfpe.13846

    Article  Google Scholar 

  3. Altuntaş, Y., Cömert, Z., Kocamaz, A.F.: Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Comput. Electron. Agric. 163, 104874 (2019). https://doi.org/10.1016/j.compag.2019.104874

    Article  Google Scholar 

  4. Altuntaş, Y., Kocamaz, A.F., Cengiz, R., Esmeray, M.: Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1–4. https://doi.org/10.1109/SIU.2018.8404800

  5. Dönmez, E.: Enhancing classification capacity of CNN models with deep feature selection and fusion: a case study on maize seed classification. Data Knowl. Eng. 141, 102075 (2022). https://doi.org/10.1016/j.datak.2022.102075

    Article  Google Scholar 

  6. Yu, L., Liu, W., Li, W., Qin, H., Xu, J., Zuo, M.: Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra. Biosyst. Eng. 172, 144–153 (2018). https://doi.org/10.1016/j.biosystemseng.2018.05.011

    Article  Google Scholar 

  7. Boote, B.W., Freppon, D.J., De La Fuente, G.N., Lübberstedt, T., Nikolau, B.J., Smith, E.A.: Haploid differentiation in maize kernels based on fluorescence imaging. Plant Breed. 135(4), 439–445 (2016). https://doi.org/10.1111/pbr.12382

    Article  Google Scholar 

  8. De La Fuente, G.N., Carstensen, J.M., Edberg, M.A., Lübberstedt, T.: Discrimination of haploid and diploid maize kernels via multispectral imaging. Plant Breed. 136(1), 50–60 (2017). https://doi.org/10.1111/pbr.12445

    Article  Google Scholar 

  9. Wang, X.-Y., Liao, W.-X., An, D., Wei, Y.-G.: Maize haploid identification via LSTM-CNN and hyperspectral imaging technology. arXiv. https://doi.org/10.48550/arXiv.1805.09105 (2018)

  10. Altuntaş, Y., Kocamaz, A.F.: Comparison of the effect of color spaces on classification performance in identification of haploid maize seeds using color moments and support vector machines. Fırat Üniversitesi Mühendis. Bilim. Derg. 31(2), 2 (2019). https://doi.org/10.35234/fumbd.585312

    Article  Google Scholar 

  11. Couto, E.G.D.O., Davide, L.M.C., Bustamante, F.D.O., Pinho, V.R.G., Silva, T.N.: Identification of haploid maize by flow cytometry, morphological and molecular markers. Ciênc. E Agrotecnologia 37(1), 25–31 (2013). https://doi.org/10.1590/s1413-70542013000100003

    Article  Google Scholar 

  12. Lin, J., Yu, L., Li, W., Qin, H.: Method for identifying maize haploid seeds by applying diffuse transmission near-infrared spectroscopy. Appl. Spectrosc. 72(4), 611–617 (2018). https://doi.org/10.1177/0003702817742790

    Article  Google Scholar 

  13. Song, P., Zhang, H., Wang, C., Luo, B., Zhang, J.X.: Design and experiment of a sorting system for haploid maize kernel. Int. J. Pattern Recognit. Artif. Intell. (2018). https://doi.org/10.1142/S0218001418550029

    Article  MathSciNet  Google Scholar 

  14. Wang, Y., et al.: Identification of maize haploid kernels based on hyperspectral imaging technology. Comput. Electron. Agric. (2018). https://doi.org/10.1016/j.compag.2018.08.012

    Article  Google Scholar 

  15. 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). https://doi.org/10.3390/APP6060183

    Article  Google Scholar 

  16. Xia, C., Yang, S., Huang, M., Zhu, Q., Guo, Y., Qin, J.: Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis. Infrared Phys. Technol. 103, 103077 (2019). https://doi.org/10.1016/J.INFRARED.2019.103077

    Article  Google Scholar 

  17. Huang, S., Fan, X., Sun, L., Shen, Y., Suo, X.: Research on classification method of maize seed defect based on machine vision. J. Sens. 2019, 1–9 (2019). https://doi.org/10.1155/2019/2716975

    Article  Google Scholar 

  18. Xu, P., Tan, Q., Zhang, Y., Zha, X., Yang, S., Yang, R.: Research on maize seed classification and recognition based on machine vision and deep learning. Agriculture 12(2), 232 (2022). https://doi.org/10.3390/AGRICULTURE12020232

    Article  Google Scholar 

  19. Aktaş, A., Demir, Ö., Doğan, B.: Tactile paving surface detection with deep learning methods. Gazi Üniversitesi Mühendis. Mimar. Fakültesi Derg. 35(3), 3 (2020). https://doi.org/10.17341/gazimmfd.652101

    Article  Google Scholar 

  20. Kilicarslan, S., Adem, K., Celik, M.: Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network. Med. Hypotheses 137, 109577 (2020). https://doi.org/10.1016/j.mehy.2020.109577

    Article  Google Scholar 

  21. Kiliçarslan, S., Celik, M.: RSigELU: a nonlinear activation function for deep neural networks’. Expert. Syst. Appl. 174, 114805 (2021). https://doi.org/10.1016/j.eswa.2021.114805

    Article  Google Scholar 

  22. Barua, P.D., et al.: Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals. Int. J. Mach. Learn. Cybern. (2022). https://doi.org/10.1007/s13042-022-01718-0

    Article  Google Scholar 

  23. Diker, A., Sönmez, Y., Özyurt, F., Avcı, E., Avcı, D.: Examination of the ECG signal classification technique DEA-ELM using deep convolutional neural network features. Multimed. Tools Appl. 80(16), 24777–24800 (2021). https://doi.org/10.1007/s11042-021-10517-8

    Article  Google Scholar 

  24. Diker, A.: An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images. Comput. Biol. Med. 148, 105635 (2022). https://doi.org/10.1016/j.compbiomed.2022.105635

    Article  Google Scholar 

  25. Kilicarslan, S., Celik, M., Sahin, Ş: Hybrid models based on genetic algorithm and deep learning algorithms for nutritional anemia disease classification. Biomed. Signal Process. Control 63, 102231 (2021). https://doi.org/10.1016/j.bspc.2020.102231

    Article  Google Scholar 

  26. Kiliçarslan, S.: PSO + GWO: a hybrid particle swarm optimization and Grey Wolf optimization based algorithm for fine-tuning hyper-parameters of convolutional neural networks for cardiovascular disease detection. J. Ambient Intell. Humaniz. Comput. (2022). https://doi.org/10.1007/s12652-022-04433-4

    Article  Google Scholar 

  27. Adem, K., Közkurt, C.: Defect detection of seals in multilayer aseptic packages using deep learning. Turk. J. Electr. Eng. Comput. Sci. 27(6), 4220–4230 (2019)

    Article  Google Scholar 

  28. Elen, A.: Covid-19 detection from radiographs by feature-reinforced ensemble learning. Concurr. Comput. Pract. Exp. 34(23), e7179 (2022). https://doi.org/10.1002/cpe.7179

    Article  Google Scholar 

  29. Ozguven, M.M., Yilmaz, G., Adem, K., Kozkurt, C.: Use of support vector machines and artificial neural network methods in variety improvement studies: potato example. Curr. Investig. Agric. Curr. Res. 6(1), 1–7 (2019)

    Google Scholar 

  30. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv. https://doi.org/10.48550/arXiv.1704.04861 (2017)

  31. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556 (2015)

  32. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  33. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, May 2019, pp. 6105–6114. Accessed: Dec. 05, 2022. [Online]. Available: https://proceedings.mlr.press/v97/tan19a.html

  34. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708. Accessed: Dec. 05, 2022. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html

  35. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. Accessed: Dec. 05, 2022. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html

  36. Kung, H.T., McDanel, B., Zhang, S.Q.: Adaptive tiling: applying fixed-size systolic arrays to sparse convolutional neural networks. In: 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 1006–1011. https://doi.org/10.1109/ICPR.2018.8545462

  37. Uçar, M.: Diagnosis of glaucoma disease using convolutional neural network architectures. Dokuz Eylül Üniversitesi Mühendis. Fakültesi Fen Ve Mühendis. Derg. 23(68), 68 (2021). https://doi.org/10.21205/deufmd.2021236815

    Article  Google Scholar 

  38. Touvron, H., et al.: ResMLP: feedforward networks for image classification with data-efficient training. IEEE Trans. Pattern Anal. Mach. Intell. (2022). https://doi.org/10.1109/TPAMI.2022.3206148

    Article  Google Scholar 

  39. Wassel, M., Hamdi, A.M., Adly, N., Torki, M.: Vision transformers based classification for glaucomatous eye condition. In: 2022 26th International Conference on Pattern Recognition (ICPR), 2022, pp. 5082–5088. https://doi.org/10.1109/ICPR56361.2022.9956086

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Authors and Affiliations

  1. Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandırma Onyedi Eylül University, 10200, Bandırma, Balıkesir, Turkey

    Emrah Dönmez, Serhat Kılıçarslan, Aykut Diker, Fahrettin Burak Demir & Abdullah Elen

  2. Department of Transportation Engineering, Faculty of Engineering and Natural Sciences, Bandırma Onyedi Eylül University, 10200, Bandırma, Balıkesir, Turkey

    Cemil Közkurt

Authors
  1. Emrah Dönmez
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  2. Serhat Kılıçarslan
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  3. Cemil Közkurt
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  4. Aykut Diker
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  5. Fahrettin Burak Demir
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  6. Abdullah Elen
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The authors contributed equally to the study.

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Correspondence to Emrah Dönmez.

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Dönmez, E., Kılıçarslan, S., Közkurt, C. et al. Identification of haploid and diploid maize seeds using hybrid transformer model. Multimedia Systems 29, 3833–3845 (2023). https://doi.org/10.1007/s00530-023-01174-y

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