Achieving high buckwheat sorting accuracy in a deep learning based model by applying fine scaling method

Abstract

Automated seed sorting is widely used in the agricultural industry. Deep learning is a new field of study in agricultural seed sorting applications. In this study, a classification of buckwheat seeds and foreign materials, such as sticks, chaff, stones was performed using deep learning. The main purpose of the study was to show the effect of scaling the images on the classification results, while creating a dataset. An industrial experimental setup was used to generate the datasets of buckwheat seeds and foreign materials to be sorted by deep learning. The images in the created dataset were rescaled with two different techniques, precision scaling and direct scaling, which were labelled as Type1 dataset and Type2 dataset, respectively. To classify buckwheat seeds and foreign materials, AlexNet architecture was used. The classification accuracy was calculated as 98.57% for Type1 Dataset and 97.34% for Type2 Dataset. As a result, it was concluded that the Type1 dataset had a higher accuracy and the use of precision scaling can be used to improve the classification results in industrial applications.

Keywords

• Image processing
• Dataset generation
• Seed sorting
• Convolutional neural networks

References

1. Aktaş H (2020) Development of Optimized Network Architectures for High Speed Industrial Applications Using Deep Learning, PhD Thesis, Akdeniz University, Antalya.
2. Aktaş H (2022) Antep fıstığının derin öğrenme ile dış kabuk rengine göre sınıflandırılması. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 11(3): 461-466. doi: 10.28948/ngumuh.1064522.
3. Devaraj A, Rathan K, Jaahnavi S, Indira K (2019) Identification of plant disease using image processing technique. In: Proceedings of the IEEE International Conference on Communication and Signal Processing. ICCSP, 749-753. doi: 10.1109/ICCSP.2019.8698056.
4. Dewi T, Mulya Z, Risma P, Oktarina Y (2021) BLOB analysis of an automatic vision guided system for a fruit picking and placing robot. International Journal of Computational Vision and Robotics 11(3), 315-327. doi: 10.1504/IJCVR.2021.115161.
5. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR, Las Vegas, NV, USA, pp. 770-778. doi: 10.1109/CVPR.2016.90.
6. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. doi: 10.48550/arXiv.1704.04861.
7. Huang KY, Cheng JF (2017) A novel auto-sorting system for Chinese cabbage seeds. Sensors 17(4): 886. doi: 10.3390/s17040886.
8. Huang S, Fan X, Sun L, Shen Y, Suo X (2019) Research on Classification Method of Maize Seed Defect Based on Machine Vision. Journal of Sensors 2019: 1-9. doi: 10.1155/2019/2716975.

9. Islam KT, Raj RG (2017) Real-time (Vision-based) road sign recognition using an artificial neural network. Sensors 17.4: 853. doi: 10.3390/s17040853.
10. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147: 70-90. doi: 10.1016/j.compag.2018.02.016.
11. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review 53(8): 5455-5516. doi: 10.1007/s10462-020-09825-6.
12. Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 1st International Conference on Computing, Communication, Control and Automation. ICCUBEA, Pune, India, pp. 768-771. doi: 10.1109/ICCUBEA.2015.153.
13. Kour VP, Arora S (2019) Fruit Disease Detection Using Rule-Based Classification. Advances in Intelligent Systems and Computing 851: 295-312. doi: 10.1007/978-981-13-2414-7_28.
14. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25: 1097-1105.
15. Kurtulmus F, Aliba İ, Kavdir I (2016) Classification of pepper seeds using machine vision based on neural network. International Journal of Agricultural and Biological Engineering 9.1: 51-62. doi: 10.3965/ijabe.v9i1.1790.
16. Kurtulmus F (2021) Identification of sunflower seeds with deep convolutional neural networks. Journal of Food Measurement and Characterization 15.2: 1024-033. doi: 10.1007/s11694-020-00707-7.
17. Li H, Li J, Guan X, Liang B, Lai Y, Luo X (2019) Research on Overfitting of Deep Learning. In: 15th International Conference on Computational Intelligence and Security (CIS) Macao, SAR, China, pp. 78-81. doi: 10.1109/CIS.2019.00025.
18. Nasiri A, Omid M, Taheri-Garavand A (2020) An automatic sorting system for unwashed eggs using deep learning. Journal of Food Engineering 283: 110036. doi: 10.1016/j.jfoodeng.2020.110036.
19. Omar N, Sengur A, Al-Ali SGS (2020) Cascaded deep learning-based efficient approach for license plate detection and recognition. Expert Systems with Applications 149: 113280 doi: 10.1016/j.eswa.2020.113280.
20. Sharma N, Jain V, Mishra A (2018) An Analysis of Convolutional Neural Networks for Image Classification. Procedia Computer Science 132: 377-384. doi: 10.1016/j.procs.2018.05.198.
21. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations. ICLR2015, San Diego, CA, USA.
22. Sun X, Wu P, Hoi SCH (2018) Face detection using deep learning: An improved faster RCNN approach. Neurocomputing 299: 42-50. doi: 10.1016/j.neucom.2018.03.030.
23. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR, Boston, MA, USA, pp. 1-9. doi: 10.1109/CVPR.2015.7298594.
24. Ter Haak M (2018) Machine learning for blob detection in high-resolution 3D microscopy images. Master thesis, KTH Royal Institute of Technology, Stockholm. doi: 10.13140/RG.2.2.10635.75045.
25. Unal Z (2020) Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis. IEEE Access 8.105587–105609. doi: 10.1109/ACCESS.2020.3000175.
26. Veeramani B, Raymond JW, Chanda P (2018) DeepSort: Deep convolutional networks for sorting haploid maize seeds. BMC Bioinformatics 19: 1-9. doi: 10.1186/s12859-018-2267-2.
27. Yusuf MD, Kusumanto R, Oktarina Y, Dewi T, Risma P (2018) BLOB Analysis for Fruit Recognition and Detection. Computer Engineering and Applications Journal, 7.1: 23-32. doi: 10.18495/comengapp.v7i1.237.
28. Zoughi T, Homayounpour MM, Deypir M (2020) Adaptive windows multiple deep residual networks for speech recognition. Expert Systems with Applications 139: 112840. doi: 10.1016/j.eswa.2019.112840.