Skip to main content
SpringerLink
Log in

CNN–SVM hybrid model for varietal classification of wheat based on bulk samples

  • Original Paper
  • Published:
European Food Research and Technology Aims and scope Submit manuscript

Abstract

Determining the variety of wheat is important to know the physical and chemical properties which may be useful in grain processing. It also affects the price of wheat in the food industry. In this study, a Convolutional Neural Network (CNN)-based model was proposed to determine wheat varieties. Images of four different piles of wheat, two of which were the bread and the remaining durum wheat, were taken and image pre-processing techniques were applied. Small-sized images were cropped from high-resolution images, followed by data augmentation. Then, deep features were extracted from the obtained images using pre-trained seven different CNN models (AlexNet, ResNet18, ResNet50, ResNet101, Inceptionv3, DenseNet201, and Inceptionresnetv2). Support Vector Machines (SVM) classifier was used to classify deep features. The classification accuracies obtained by classification with various kernel functions such as Linear, Quadratic, Cubic and Gaussian were compared. The highest wheat classification accuracy was achieved with the deep features extracted with the Densenet201 model. In the classification made with the Cubic kernel function of SVM, the accuracy value was 98.1%.

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

References

  1. Dixon J, Braun H-J, Kosina P, Crouch JH (2009) Wheat facts and futures 2009. Cimmyt

  2. Shewry PR, Hey SJ (2015) The contribution of wheat to human diet and health. Food Energy Security 4(3):178–202

    Article  PubMed  Google Scholar 

  3. FAO: http://www.fao.org/home/en/ (2019). Accessed

  4. Lopes M, Reynolds M, Manes Y, Singh R, Crossa J, Braun H (2012) Genetic yield gains and changes in associated traits of CIMMYT spring bread wheat in a “historic” set representing 30 years of breeding. Crop Sci 52(3):1123–1131

    Article  Google Scholar 

  5. Žilić S, Barać M, Pešić M, Dodig D, Ignjatović-Micić D (2011) Characterization of proteins from grain of different bread and durum wheat genotypes. Int J Mol Sci 12(9):5878–5894

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Kimber G, Sears ER (1987) Evolution in the genus Triticum and the origin of cultivated wheat. Wheat and wheat improvement, vol 13. Soil Science Society of America, pp 154–164

    Google Scholar 

  7. Sayaslan A, Koyuncu M, Yildirim A, Güleç T, Sönmezoğlu ÖA, Kandemir N (2012) Some quality characteristics of selected durum wheat (Triticum durum) landraces. Turk J Agric For 36(6):749–756

    CAS  Google Scholar 

  8. Botwright T, Condon A, Rebetzke G, Richards R (2002) Field evaluation of early vigour for genetic improvement of grain yield in wheat. Aust J Agric Res 53(10):1137–1145

    Article  Google Scholar 

  9. Dholakia B, Ammiraju J, Singh H, Lagu M, Röder M, Rao V et al (2003) Molecular marker analysis of kernel size and shape in bread wheat. Plant Breeding 122(5):392–395

    Article  CAS  Google Scholar 

  10. Mahajan S, Das A, Sardana HK (2015) Image acquisition techniques for assessment of legume quality. Trends Food Sci Technology 42(2):116–133

    Article  CAS  Google Scholar 

  11. Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electr Agric 153:69–81

    Article  Google Scholar 

  12. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  13. Dong M, Mu S, Shi A, Mu W, Sun W (2020) Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network. Int J Agric Biol Eng 13(4):205–210

    Google Scholar 

  14. Jahan N, Flores P, Liu Z, Friskop A, Mathew JJ, Zhang Z (2020) Detecting and Distinguishing Wheat Diseases using Image Processing and Machine Learning Algorithms. 2020 ASABE Annual International Virtual Meeting. American Society of Agricultural and Biological Engineers, p 1

  15. Manavalan R (2020) Automatic identification of diseases in grains crops through computational approaches: a review. Comput Electr Agric 178:105802

    Article  Google Scholar 

  16. Basati Z, Jamshidi B, Rasekh M, Abbaspour-Gilandeh Y (2018) Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition. Spectrochimica Acta Part A 203:308–314

    Article  CAS  Google Scholar 

  17. Davari A, Parker BL (2018) A review of research on Sunn Pest Eurygaster integriceps Puton (Hemiptera: Scutelleridae) management published 2004–2016. J Asia-Pacific Entomol 21(1):352–360

    Article  Google Scholar 

  18. Huang L, Li T, Ding C, Zhao J, Zhang D, Yang G (2020) Diagnosis of the severity of fusarium head blight of wheat ears on the basis of image and spectral feature fusion. Sensors 20(10):2887

    Article  PubMed Central  Google Scholar 

  19. Sabanci K (2020) Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques. J Sci Food Agric 100(2):817–824

    Article  CAS  PubMed  Google Scholar 

  20. Hasan MM, Chopin JP, Laga H, Miklavcic SJ (2018) Detection and analysis of wheat spikes using convolutional neural networks. Plant Methods 14(1):1–13

    Article  Google Scholar 

  21. Ma J, Li Y, Du K, Zheng F, Zhang L, Gong Z et al (2020) Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Comput Electr Agric 168:105159

    Article  Google Scholar 

  22. Rasti S, Bleakley CJ, Silvestre GC, Holden N, Langton D, O’Hare GM (2020) Crop growth stage estimation prior to canopy closure using deep learning algorithms. Neural Computing Applications, pp 1–11

  23. Velumani K, Madec S, de Solan B, Lopez-Lozano R, Gillet J, Labrosse J et al (2020) An automatic method based on daily in situ images and deep learning to date wheat heading stage. Field Crop Res 252:107793

    Article  Google Scholar 

  24. Belton D, Helmholz P, Long J, Zerihun A (2019) Crop height monitoring using a consumer-grade camera and UAV technology. PFG J Photogram Remote Sens Geoinfo Sci 87(5):249–262

    Google Scholar 

  25. Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS et al (2017) Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience. 6(10):gix083

    Article  Google Scholar 

  26. Toda Y, Okura F, Ito J, Okada S, Kinoshita T, Tsuji H et al (2020) Training instance segmentation neural network with synthetic datasets for crop seed phenotyping. Commun Biol 3(1):1–12

    Article  Google Scholar 

  27. Zhang C, Si Y, Lamkey J, Boydston RA, Garland-Campbell KA, Sankaran S (2018) High-throughput phenotyping of seed/seedling evaluation using digital image analysis. Agronomy 8(5):63

    Article  Google Scholar 

  28. Adnan M, Abaid-ur-Rehman M, Latif MA, Ahmad N, Nazir M, Akhter N (2018) Mapping wheat crop phenology and the yield using machine learning (ML). Int J Adv Comput Sci Appl 9(8):301–306

    Google Scholar 

  29. Bijanzadeh E, Emam Y, Ebrahimie E (2010) Determining the most important features contributing to wheat grain yield using supervised feature selection model. Aust J Crop Sci 4(6):402–407

    Google Scholar 

  30. Kadir MKA, Ayob MZ, Miniappan N (2014) Wheat yield prediction: artificial neural network based approach. 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T): IEEE, pp 161–165

  31. Moghimi A, Yang C, Anderson JA (2020) Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Comput Electron Agric 172:105299

    Article  Google Scholar 

  32. Olgun M, Onarcan AO, Özkan K, Işik Ş, Sezer O, Özgişi K et al (2016) Wheat grain classification by using dense SIFT features with SVM classifier. Comput Electron Agric 122:185–190

    Article  Google Scholar 

  33. Sabanci K, Aslan MF, Durdu A (2020) Bread and durum wheat classification using wavelet based image fusion. J Sci Food Agric 100(15):5577–5585

    Article  CAS  PubMed  Google Scholar 

  34. 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 

  35. Taner A, Öztekin YB, Tekgüler A, Sauk H, Duran H (2018) Classification of varieties of grain species by artificial neural networks. Agronomy 8(7):123

    Article  Google Scholar 

  36. Camargo A, Smith J (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys Eng 102(1):9–21

    Article  Google Scholar 

  37. Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: a fruit detection system using deep neural networks. Sensors 16(8):1222

    Article  PubMed Central  Google Scholar 

  38. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419

    Article  PubMed  PubMed Central  Google Scholar 

  39. Özkan K, Işık Ş, Yavuz BT (2019) Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples. J Sci Food Agric 99(11):4977–4984

    Article  PubMed  CAS  Google Scholar 

  40. Zhang L, Sun H, Rao Z, Ji H (2020) Non-destructive identification of slightly sprouted wheat kernels using hyperspectral data on both sides of wheat kernels. Biosys Eng 200:188–199

    Article  Google Scholar 

  41. Cinar I, Koklu M (2019) Classification of rice varieties using artificial intelligence methods. Int J Intell Syst Appl Eng 7(3):188–194

    Article  Google Scholar 

  42. Bloice MD, Stocker C, Holzinger A (2017) Augmentor: an image augmentation library for machine learning. arXiv preprint arXiv:04680

  43. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1–48

    Article  Google Scholar 

  44. Lopes U, Valiati JF (2017) Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput Biol Med 89:135–143

    Article  CAS  PubMed  Google Scholar 

  45. Ismael AM, Şengür A (2021) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl 164:114054

    Article  PubMed  Google Scholar 

  46. Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) CNN-based transfer learning–BiLSTM network: a novel approach for COVID-19 infection detection. Appl Soft Comput 98:106912

    Article  PubMed  Google Scholar 

  47. Koklu M, Unlersen MF, Ozkan IA, Aslan MF, Sabanci K (2022) A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement 188:110425

    Article  Google Scholar 

  48. Sabanci K, Aslan MF, Ropelewska E, Unlersen MF (2021) A convolutional neural network-based comparative study for pepper seed classification: analysis of selected deep features with support vector machine. J Food Process Eng e13955. https://doi.org/10.1111/jfpe.13955

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Turkey

    Muhammed Fahri Unlersen

  2. Department of Bioengineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Mesut Ersin Sonmez, Bedrettin Demir & Nevzat Aydin

  3. Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Muhammet Fatih Aslan & Kadir Sabanci

  4. Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100, Skierniewice, Poland

    Ewa Ropelewska

Authors
  1. Muhammed Fahri Unlersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mesut Ersin Sonmez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammet Fatih Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bedrettin Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nevzat Aydin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kadir Sabanci
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ewa Ropelewska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ewa Ropelewska.

Ethics declarations

Conflict of interest

The authors declared that they have no conflict of interest.

Compliance with ethics requirements

This study does not contain any studies with human participants or animals performed by any authors.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Unlersen, M.F., Sonmez, M.E., Aslan, M.F. et al. CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Eur Food Res Technol 248, 2043–2052 (2022). https://doi.org/10.1007/s00217-022-04029-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00217-022-04029-4

Keywords

Search

Navigation