A Deep Learning-Based Seed Classification with Mobile Application

Abstract

Seed quality is an essential factor in agricultural production. Some seeds are inherently small so it is difficult to identify and classify differences between species. In the traditional method, these differences are classified by experts considering the morphological structure, texture and color. This method involves a classification process that is costly, subjective and time confusing, what makes it necessary to develop a process that can automatically detect the type of seeds. In this study, a mobile application has been developed that quickly detects and classifies seed images with high accuracy using CNN, one of the deep learning techniques.

Keywords

• Deep learning
• convolutional neural networks
• seed classification
• mobile application

References

1. [1] Ali, A., Qadri, S., Mashwani,W.K., Brahim, B.S., Naeem, S., et al., Machine learning approach for the classification of corn seed using hybrid features, Int. J. Food Prop., (2020), 1110–1124.
2. [2] Bengio, Y., Simard, P., Frasconi, P., Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Networks, 5(2)(1994), 157–166.
3. [3] Chollet, F., Xception: deep learning with depthwise separable convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 1800–1807.
4. [4] Dourado, C.M.J.M., da Silva, S.P.P., da Nobrega, R.V.M., Antonio, A.C., Filho, P.P.R., et al., Deep learning IoT system for online stroke detection in skull computed tomography images, Comput. Networks, 152(2019), 25–39.
5. [5] Ferdouse, A.F.M., Shakirul, I.M., Abujar, S., Akhter, H.S., A novel approach for tomato diseases classification based on deep convolutional neural networks, Proceedings of International Joint Conference on Computational Intelligence, (2020), 583–591.
6. [6] Gulzar, Y., Hamid, Y., Soomro, A.B., Alwan, A.A., Journaux, L., A convolution neural network-based seed classification system, Symmetry 2020, 12(12)(2020).
7. [7] Kayıkçı, Ş., Başol, Y., Dörter, E., Classification of turkish cuisine with deep learning on mobile platform, UBMK 2019 - Proceedings, 4th Int. Conf. Comput. Sci. Eng., (2019), 296–300.
8. [8] Keya, M., Majumdar, B., Islam, M.S., A robust deep learning segmentation and identification approach of different bangladeshi plant seeds using CNN, 11th International Conference on Computing, Communication and Networking, (2020), 1–6.

9. [9] Kiratiratanapruk, K., Temniranrat, P., Sinthupinyo, W., Prempree, P., Chaitavon, K., et al., Development of paddy rice seed classification process using machine learning techniques for automatic grading machine, Journal of Sensors, (2020), 1–14.
10. [10] Koklu, M., Ozkan, I.A., Multiclass classification of dry beans using computer vision and machine learning techniques, Computers and Electronics in Agriculture, 174(2020).
11. [11] Lammie, C., Olsen, A., Carrick, T., Rahimi, A.M., Low-power and high-speed deep FPGA inference engines for weed classification at the edge, IEEE Access, (2019).
12. [12] Langkvist, M., Karlsson, L., Loutfi, A., A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognition Letters, 42(1)(2014), 11–24.
13. [13] Powers, D.M.W., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, ArXiv abs/2010.16061, (2020).
14. [14] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., et al., ImageNet large scale visual recognition challenge, International Journal of Computer Vision, 115(3)(2015), 211–252.
15. [15] Salas, A.H., Morzan-Samame, J., Nunez-del-Prado, M., Crime alert! crime typification in news based on text mining, Lecture Notes in Networks and Systems, 69(2020), 725–741.
16. [16] Salimi, Z., Boelt, B., Classification of processing damage in sugar beet (Beta vulgaris) seeds by multispectral image analysis, Sensors (Switzerland), 19(10)(2019).
17. [17] Santos, L., Santos, F.N., Oliveira, P.M., Shinde, P., Deep learning applications in agriculture: a short review, Robot 2019: Fourth Iberian Robotics Conference Advances in Intelligent Systems and Computing, 1092(2020), 139–151.
18. [18] Schmidhuber, J., Deep learning in neural networks: an overview, Neural Networks, 61(2015), 85–117.
19. [19] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., et al., Going deeper with convolutions, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., (2015), 1–9.
20. [20] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., (2016), 2818–2826.
21. [21] Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., Inception-v4, Inception-ResNet and the impact of residual connections on learning, 31st AAAI Conf. Artif. Intell., (2017), 4278–4284.
22. [22] Şeker, A., Diri, B., Balık, H.H., A review about deep learning methods and applications, Gazi M¨uhendislik Bilim. Dergi., 3(3)(2017), 47–64.
23. [23] Verma, S., Chug, A., Singh, A.P., Sharma, S., Rajvanshi, P., Deep learning-based mobile application for plant disease diagnosis, Applications of Image Processing and Soft Computing Systems in Agriculture, (2019), 242–271.
24. [24] Wu, S., Zhong, S., Liu, Y., Deep residual learning for image steganalysis, Multimedia Tools and Applications, 77(2017), 10437–10453.