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Classification of bread wheat varieties with a combination of deep learning approach

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Abstract

Wheat is one of the most produced and consumed grain products worldwide. Wheat is the main grain product in developed and underdeveloped countries. Flour obtained from wheat is used in the production of bread, the most basic food product, and in the production of cakes used to celebrate the most special days. Therefore, knowing the pure bread wheat varieties is important both for production and for those who use wheat as flour. However, since wheat varieties are very similar to each other, it is difficult to distinguish them. To solve this problem, a pre-trained hybrid model based on convolutional neural network (CNN) is proposed in this study to classify bread wheat varieties. Images of five different registered bread wheat varieties were captured and a bread wheat image data set was created by separating them with image processing techniques to be used in deep learning. Then, the obtained images were classified using transfer learning with fine-tuning on the Xception model, one of the pre-trained CNN models. To increase the classification success, Xception CNN model and BiLSTM (Bidirectional Long Short-Term Memory) algorithms hybrid (Xception + BiLSTM) models were obtained. As a result of classifications, the highest classification success was obtained from the Xception + BiLSTM model with 97.73%. The results revealed that the proposed methods can be used in systems used for classification of bread wheat varieties and to obtain pure wheat varieties automatically.

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Data availability

The dataset will be shared at the online address www.aliyasar.com. All data and program files included in this study are available upon request by contact with the corresponding author.

References

  1. Unlersen MF et al (2022) CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Eur Food Res Technol 248(8):2043–2052

    Article  CAS  Google Scholar 

  2. Malegori C et al (2018) GlutoPeak profile analysis for wheat classification: skipping the refinement process. J Cereal Sci 79:73–79

    Article  CAS  Google Scholar 

  3. Sabanci K, Kayabasi A, Toktas A (2017) Computer vision-based method for classification of wheat grains using artificial neural network. J Sci Food Agric 97(8):2588–2593

    Article  CAS  PubMed  Google Scholar 

  4. Delwiche SR, Massie DR (1996) Classification of wheat by visible and near-infrared reflectance from single kernels. Cereals and Grains Association

    Google Scholar 

  5. Romero JR et al (2013) Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Comput Electron Agric 96:173–179

    Article  Google Scholar 

  6. Olgun M et al (2016) Wheat grain classification by using dense SIFT features with SVM classifier. Comput Electron Agric 122:185–190

    Article  Google Scholar 

  7. Loddo A, Loddo M, Di Ruberto C (2021) A novel deep learning based approach for seed image classification and retrieval. Comput Electron Agric 187:106269

    Article  Google Scholar 

  8. Pourreza A et al (2012) Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput Electron Agric 83:102–108

    Article  Google Scholar 

  9. Singh P et al (2020) Classification of wheat seeds using image processing and fuzzy clustered random forest. Int J Agric Resour Gov Ecol 16(2):123–156

    Google Scholar 

  10. Aznan A et al (2017) Rice seed varieties identification based on extracted colour features using image processing and artificial neural network (ANN). Int J Adv Sci Eng Inf Technol 7(2220):10.18517

    Google Scholar 

  11. Kinnikar A, Desai P, Jahagirdar S (2015) Identification and detection of seed borne diseases of soybean using image processing-a survey. Int J Emerg Technol Comput Sci Electron 14:363–368

    Google Scholar 

  12. Wankhede DS et al (2022) A study on identification of plant diseases using image processing. Computer networks, big data and IoT. Springer, pp 463–477

    Chapter  Google Scholar 

  13. Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning—a review. IEEE Access 9:56683–56698

    Article  Google Scholar 

  14. Balram MG, Kumar KK (2022) Crop field monitoring and disease detection of plants in smart agriculture using internet of things. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2022.0130795

    Article  Google Scholar 

  15. Kassem MA et al (2021) Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review. Diagnostics 11(8):1390

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kilicarslan S, Celik M, Sahin Ş (2021) Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification. Biomed Signal Process Control 63:102231

    Article  Google Scholar 

  17. Yogapriya J et al (2021) Gastrointestinal tract disease classification from wireless endoscopy images using pretrained deep learning model. Comput Math Methods Med. https://doi.org/10.1155/2021/5940433

    Article  PubMed  PubMed Central  Google Scholar 

  18. Balci M et al (2022) Machine learning-based detection of sleep-disordered breathing type using time and time-frequency features. Biomed Signal Process Control 73:103402

    Article  Google Scholar 

  19. Altaheri H et al (2021) Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06352-5

    Article  Google Scholar 

  20. Liu X et al (2021) Deep learning in ECG diagnosis: a review. Knowl-Based Syst 227:107187

    Article  Google Scholar 

  21. Ak A, Topuz V, Midi I (2022) Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator. Biomed Signal Process Control 72:103295

    Article  Google Scholar 

  22. Laschowski B et al (2022) Environment classification for robotic leg prostheses and exoskeletons using deep convolutional neural networks. Front Neurorobot 15:730965

    Article  PubMed  PubMed Central  Google Scholar 

  23. Luongo F et al (2021) Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. Surgery 169(5):1240–1244

    Article  PubMed  Google Scholar 

  24. Tural S et al (2022) Deep learning based classification of military cartridge cases and defect segmentation. IEEE Access 10:74961–74976

    Article  Google Scholar 

  25. Kong L, Wang J, Zhao P (2022) YOLO-G: a lightweight network model for improving the performance of military targets detection. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3177628

    Article  Google Scholar 

  26. Chandra R, Jain A, Singh Chauhan D (2022) Deep learning via LSTM models for COVID-19 infection forecasting in India. PLoS ONE 17(1):e0262708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ketu S, Mishra PK (2022) India perspective: CNN–LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability. Soft Comput 26(2):645–664

    Article  PubMed  Google Scholar 

  28. Mehtab S, Sen J (2022) Analysis and forecasting of financial time series using CNN and LSTM-based deep learning models. Advances in distributed computing and machine learning. Springer, pp 405–423

    Chapter  Google Scholar 

  29. Khademi Z, Ebrahimi F, Kordy HM (2022) A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals. Comput Biol Med 143:105288

    Article  PubMed  Google Scholar 

  30. Gaafar AS, Dahr JM, Hamoud AK (2022) Comparative analysis of performance of deep learning classification approach based on LSTM-RNN for textual and image datasets. Informatica. https://doi.org/10.31449/inf.v46i5.3872

    Article  Google Scholar 

  31. Gill HS, Khehra BS (2022) An integrated approach using CNN–RNN–LSTM for classification of fruit images. Mater Today Proceed 51:591–595

    Article  Google Scholar 

  32. Sabancı K, Akkaya M (2016) Classification of different wheat varieties by using data mining algorithms. Int J Intell Syst Appl Eng 4(2):40–44

    Article  Google Scholar 

  33. Pazoki A, Pazoki Z (2011) Classification system for rain fed wheat grain cultivars using artificial neural network. Afr J Biotech 10(41):8031–8038

    Article  Google Scholar 

  34. Khojastehnazhand M, Roostaei M (2022) Classification of seven Iranian wheat varieties using texture features. Expert Syst Appl 199:117014

    Article  Google Scholar 

  35. Gao J et al (2021) Identification method of wheat cultivars by using a convolutional neural network combined with images of multiple growth periods of wheat. Symmetry 13(11):2012

    Article  Google Scholar 

  36. Sabanci K et al (2022) A novel convolutional-recurrent hybrid network for sunn pest-damaged wheat grain detection. Food Anal Methods 15(6):1748–1760

    Article  Google Scholar 

  37. Shaheed K et al (2022) DS-CNN: a pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Syst Appl 191:116288

    Article  Google Scholar 

  38. Girsang ND (2021) Literature study of convolutional neural network algorithm for batik classification. Brilliance 1(1):1–7

    Article  Google Scholar 

  39. Acharya UR et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396

    Article  PubMed  Google Scholar 

  40. Li D et al (2021) BLSTM and CNN stacking architecture for speech emotion recognition. Neural Process Lett 53(6):4097–4115

    Article  Google Scholar 

  41. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition

  42. Lo WW, Yang X, Wang Y (2019) An xception convolutional neural network for malware classification with transfer learning. In 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE

  43. Jinsakul N et al (2019) Enhancement of deep learning in image classification performance using xception with the swish activation function for colorectal polyp preliminary screening. Mathematics 7(12):1170

    Article  Google Scholar 

  44. Lu W et al (2021) A CNN–BiLSTM–AM method for stock price prediction. Neural Comput Appl 33(10):4741–4753

    Article  Google Scholar 

  45. He Y et al (2019) Application of CNN–LSTM in gradual changing fault diagnosis of rod pumping system. Math Probl Eng 2019:1–9

    Google Scholar 

  46. Yasar A, Saritas I, Korkmaz H (2019) Computer-aided diagnosis system for detection of stomach cancer with image processing techniques. J Med Syst 43(4):1–11

    Article  Google Scholar 

  47. Golcuk A et al (2023) Classification of Cicer arietinum varieties using MobileNetV2 and LSTM. Eur Food Res Technol. https://doi.org/10.1007/s00217-023-04217-w

    Article  Google Scholar 

  48. Yasar A (2023) Wrapper and hybrid feature selection methods using metaheuristic algorithm for chest x-ray images classification: COVID-19 as a case study. Tehnički glasnik 17(3):313–323

    Article  Google Scholar 

  49. Tuğrul B (2022) Classification of five different rice seeds grown in Turkey with deep learning methods. Commun Fac Sci Univ Ank Series A2-A3 Phys Sci Eng. 64(1):40–50

    Google Scholar 

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

    Article  Google Scholar 

  51. Hamid Y et al. (2022) Smart seed classification system based on MobileNetV2 architecture. In 2022 2nd International Conference on Computing and Information Technology (ICCIT). IEEE

  52. Kishore B et al (2022) Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Comput Intell Neurosci. https://doi.org/10.1155/2022/2062944

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chen J et al (2022) A vision transformer network SeedViT for classification of maize seeds. J Food Process Eng 45(5):e13998

    Article  Google Scholar 

  54. Sabanci K et al (2022) A convolutional neural network-based comparative study for pepper seed classification: analysis of selected deep features with support vector machine. J Food Process Eng 45(6):e13955

    Article  CAS  Google Scholar 

  55. Jaithavil D, Triamlumlerd S, Pracha M (2022) Paddy seed variety classification using transfer learning based on deep learning. In 2022 International Electrical Engineering Congress (iEECON). IEEE

  56. Golcuk A, Yasar A (2023) Classification of bread wheat genotypes by machine learning algorithms. J Food Compos Anal. https://doi.org/10.1016/j.jfca.2023.105253

    Article  Google Scholar 

  57. Yasar A (2023) Benchmarking analysis of CNN models for bread wheat varieties. Eur Food Res Technol 249(3):749–758

    Article  CAS  Google Scholar 

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Acknowledgements

I would like to thank the Ministry of Agriculture and Forestry of the Republic of Türkiye, Directorate of the Central Research Institute of Field Crops for their contributions.

Funding

This research received no external funding.

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Authors and Affiliations

  1. Department of Mechatronic Engineering, Faculty of Technology, Selcuk University, Konya, Turkey

    Ali Yasar

  2. Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey

    Adem Golcuk

  3. Department of Computer Science, University of Portsmouth, Portsmouth, UK

    Omer Faruk Sari

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  1. Ali Yasar
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  2. Adem Golcuk
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Correspondence to Adem Golcuk.

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Yasar, A., Golcuk, A. & Sari, O.F. Classification of bread wheat varieties with a combination of deep learning approach. Eur Food Res Technol 250, 181–189 (2024). https://doi.org/10.1007/s00217-023-04375-x

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