U2-NET SEGMENTATION AND MULTI-LABEL CNN CLASSIFICATION OF WHEAT VARIETIES

Year 2024, Volume: 12 Issue: 2, 358 - 372, 01.06.2024

Mustafa Şamil Argun , Fuat Türk , Zafer Civelek

https://doi.org/10.36306/konjes.1364509

Abstract

There are many varieties of wheat grown around the world. In addition, they have different physiological states such as vitreous and yellow berry. These reasons make it difficult to classify wheat by experts. In this study, a workflow was carried out for both segmentation of wheat according to its vitreous/yellow berry grain status and classification according to variety. Unlike previous studies, automatic segmentation of wheat images was carried out with the U2-NET architecture. Thus, roughness and shadows on the image are minimized. This increased the level of success in classification. The newly proposed CNN architecture is run in two stages. In the first stage, wheat was sorted as vitreous-yellow berry. In the second stage, these separated wheats were grouped by multi-label classification. Experimental results showed that the accuracy for binary classification was 98.71% and the multi-label classification average accuracy was 89.5%. The results showed that the proposed study has the potential to contribute to making the wheat classification process more reliable, effective, and objective by helping the experts.

Keywords

Wheat Segmentation with U2-NET, U2-NET Architecture, Multi-Label CNN Classification, Wheat Classification

References

• I. G. C. (IGC). International Grain Council Grain Market Report [Online] Available: https://www.igc.int/ [Accessed Sept.21, 2023].
• M. Feldman, "Origin of cultivated wheat," The World Wheat Book, A history of wheat breeding, 2000.
• P. R. Shewry, "Do ancient types of wheat have health benefits compared with modern bread wheat?," Journal of Cereal Science, vol. 79, pp. 469-476, 2018/01/01/ 2018, doi: https://doi.org/10.1016/j.jcs.2017.11.010.
• F. Özberk, A. Karagöz, İ. Özberk, and A. Ayhan, "Buğday genetik kaynaklarından yerel ve kültür çeşitlerine; Türkiye'de buğday ve ekmek," Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, vol. 25, no. 2, pp. 218-233, 2016.
• J. Ammiraju et al., "Inheritance and identification of DNA markers associated with yellow berry tolerance in wheat (Triticum aestivum L.)," Euphytica, vol. 123, no. 2, pp. 229-233, 2002.
• G. A. López‐Ahumada et al., "Physicochemical characteristics of starch from bread wheat (Triticum aestivum) with “yellow berry”," Starch‐Stärke, vol. 62, no. 10, pp. 517-523, 2010.
• J. Dexter, B. Marchylo, A. MacGregor, and R. Tkachuk, "The structure and protein composition of vitreous, piebald and starchy durum wheat kernels," Journal of Cereal Science, vol. 10, no. 1, pp. 19-32, 1989.
• J. Dexter, P. Williams, N. Edwards, and D. Martin, "The relationships between durum wheat vitreousness, kernel hardness and processing quality," Journal of Cereal Science, vol. 7, no. 2, pp. 169-181, 1988.
• R. C. Hoseney, Principles of cereal science and technology. A general reference on cereal foods. American Association of Cereal Chemists, Inc., 1986.
• F. Dowell, "Differentiating vitreous and nonvitreous durum wheat kernels by using near‐infrared spectroscopy," Cereal Chem., vol. 77, no. 2, pp. 155-158, 2000.
• X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. R. Zaiane, and M. Jagersand, "U2-Net: Going deeper with nested U-structure for salient object detection," Pattern Recognition, vol. 106, p. 107404, 2020/10/01/ 2020, doi: https://doi.org/10.1016/j.patcog.2020.107404.
• M. Lüy, F. Türk, M. Ş. Argun, and T. Polat, "Investigation of the effect of hectoliter and thousand grain weight on variety identification in wheat using deep learning method," Journal of Stored Products Research, vol. 102, p. 102116, 2023/05/01/ 2023, doi: https://doi.org/10.1016/j.jspr.2023.102116.
• T. Fuat and Y. Kökver, "Application with deep learning models for COVID-19 diagnosis," Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 2, pp. 169-180, 2022.
• M. Gour, S. Jain, and T. Sunil Kumar, "Residual learning based CNN for breast cancer histopathological image classification," International Journal of Imaging Systems and Technology, vol. 30, no. 3, pp. 621-635, 2020.
• G. Madhulatha and O. Ramadevi, "Recognition of plant diseases using convolutional neural network," in 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC), 2020: IEEE, pp. 738-743.
• A. Pande, M. Munot, R. Sreeemathy, and R. Bakare, "An efficient approach to fruit classification and grading using deep convolutional neural network," in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019: IEEE, pp. 1-7.
• A. Kausar, M. Sharif, J. Park, and D. R. Shin, "Pure-cnn: A framework for fruit images classification," in 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018: IEEE, pp. 404-408.
• C. Narvekar and M. Rao, "Flower classification using CNN and transfer learning in CNN-Agriculture Perspective," in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020: IEEE, pp. 660-664.
• A. Taner, Y. B. Öztekin, and H. Duran, "Performance analysis of deep learning CNN models for variety classification in hazelnut," Sustainability, vol. 13, no. 12, p. 6527, 2021.
• N.-F. Huang, D.-L. Chou, and C.-A. Lee, "Real-time classification of green coffee beans by using a convolutional neural network," in 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), 2019: IEEE, pp. 107-111.
• M. Momeny, A. Jahanbakhshi, K. Jafarnezhad, and Y.-D. Zhang, "Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach," Postharvest Biol. Technol., vol. 166, p. 111204, 2020.
• A. Jahanbakhshi, M. Momeny, M. Mahmoudi, and Y.-D. Zhang, "Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks," Scientia Horticulturae, vol. 263, p. 109133, 2020.
• B. Jabir and N. Falih, "Deep learning-based decision support system for weeds detection in wheat fields," International Journal of Electrical and Computer Engineering, vol. 12, no. 1, p. 816, 2022.
• H. Zheng, G. Wang, and X. Li, "Swin-MLP: a strawberry appearance quality identification method by Swin Transformer and multi-layer perceptron," Journal of Food Measurement and Characterization, pp. 1-12, 2022.
• Y. Shen, Y. Yin, B. Li, C. Zhao, and G. Li, "Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks," Comput. Electron. Agric., vol. 181, p. 105931, 2021.
• S. Lingwal, K. K. Bhatia, and M. S. Tomer, "Image-based wheat grain classification using convolutional neural network," Multimedia Tools and Applications, vol. 80, no. 28, pp. 35441-35465, 2021.
• A. Yasar, "Benchmarking analysis of CNN models for bread wheat varieties," Eur. Food Res. Technol., pp. 1-10, 2022.
• K. Hacıefendioğlu, A. F. Genc, S. Nayır, S. Ayas, and A. C. Altunışık, "Automatic Estimation of Post-fire Compressive Strength Reduction of Masonry Structures Using Deep Convolutional Neural Network," Fire Technology, vol. 58, no. 5, pp. 2779-2809, 2022.
• K. Sabanci, A. Toktas, and A. Kayabasi, "Grain classifier with computer vision using adaptive neuro‐fuzzy inference system," Journal of the Science of Food and Agriculture, vol. 97, no. 12, pp. 3994-4000, 2017.
• T.-Y. Kuo, C.-L. Chung, S.-Y. Chen, H.-A. Lin, and Y.-F. Kuo, "Identifying rice grains using image analysis and sparse-representation-based classification," Comput. Electron. Agric., vol. 127, pp. 716-725, 2016.
• A. Soleimanipour, M. Azadbakht, and A. Rezaei Asl, "Cultivar identification of pistachio nuts in bulk mode through EfficientNet deep learning model," Journal of Food Measurement and Characterization, pp. 1-11, 2022.
• U. Shafi et al., "Embedded AI for Wheat Yellow Rust Infection Type Classification," IEEE Access, vol. 11, pp. 23726-23738, 2023.