Classification of five different rice seeds grown in Turkey with deep learning methods
Classification of five different rice seeds grown in Turkey with deep learning methods
The increase in the world population and harmful environmental factors such as global warming necessitate a change in agricultural practices with the traditional method. Precision agriculture solutions offer many innovations to meet this increasing need. Using healthy, suitable and high-quality seeds is the first option that comes to mind in order to harvest more products from the fields. Seed classification is carried out in a labor-intensive manner. Due to the nature of this process, it is error-prone and also requires a high budget and time. The use of state-of-the-art methods such as Deep Learning in computer vision solutions enables the development of different applications in many areas. Rice is the most widely used grain worldwide after wheat and barley. This study aims to classify five different rice species grown in Turkey using four different Convolutional Neural Network (CNN) architectures. First, a new rice image dataset of five different species was created. Then, known and widely applied CNN architectures such as Visual Geometry Group (VGG), Residual Network (ResNet) and EfficientNets were trained and results were obtained. In addition, a new CNN architecture was designed and the results were compared with the other three architectures. The results showed that the VGG architecture generated the best accuracy value of 97%.
Keywords:
Convolutional neural networks, VGG, ResNet EfficientNet,
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- Chen, C., He, W., Nassirou, T. Y., Nsabiyumva, A., Dong, X., Adedze, Y. M. N., Jin, D., Molecular characterization and genetic diversity of different genotypes of Oryza sativa and Oryza glaberrima, Electron. J. Biotechnol., 30 (2017), 48–57, https://doi.org/10.1016/j.ejbt.2017.08.001.
- Taşlıgil, N., Şahin, G., Türkiye’de çeltik (Oryza sativa L.) yetiştiriciliği ve coğrafi dağılımı, Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6 (2011), 182–203, https://doi.org/10.14520/adyusbd.105.
- Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E., Deep learning for computer vision: A brief review, Comput. Intell. Neurosci., 2018 (2018), https://doi.org/10.1155/2018/7068349.
- Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A. A., Generative adversarial networks: An overview, IEEE Signal Process Mag., 35 (1) (2018), 53–65, https://doi.org/10.1109/MSP.2017.2765202.
- Huang, K., Chien, M., A novel method of identifying paddy seed varieties, Sensors, 17 (4) (2017), 809–816, https://doi.org/10.3390/s17040809.
- Ali, T., Jhandhir, Z., Ahmad, A., Khan, M., Khan, A. A., Choi, G. S., Detecting fraudulent labeling of rice samples using computer vision and fuzzy knowledge, Multimed. Tools Appl., 76 (23) (2017), 24675–24704, https://doi.org/10.1007/s11042-017-4472-9.
- Qiu, Z., Chen, J., Zhao, Y., Zhu, S., He, Y., Zhang, C.,, Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural networks, Appl. Sci., 8 (2) (2018), 212–223, https://doi.org/10.3390/app8020212.
- Kiratiratanapruk, K., Temniranrat, P., Sinthupinyo, W., Prempree, P., Chaitavon, K., Porntheeraphat, S., Prasertsak, A., Development of paddy rice seed classification process using machine learning techniques for automatic grading machine, J. Sens., 2020 (2020), https://doi.org/10.1155/2020/7041310.
- Hoang, V. T., Van Hoai, D. P., Surinwarangkoon, T., Duong, H. T., Meethongjan, K., A comparative study of rice variety classification based on deep learning and hand-crafted features, ECTI Transactions on Computer and Information Technology (ECTI-CIT), 14 (1) (2020), 1–10, https://doi.org/10.37936/ecti-cit.2020141.204170.
- Gilanie, G., Nasir, N., Bajwa, U. I., Ullah, H., RiceNet: Convolutional neural networks-based model to classify Pakistani grown rice seed types, Multimed. Syst., 27 (5) (2021), 867–875, https://doi.org/10.1007/s00530-021-00760-2.
- Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, (2014), https://doi.org/10.48550/arXiv.1409.1556.
- Altuntaş, Y., Cömert, Z. Kocamaz, A. F., Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach, Comput. Electron. Agric., 163 (2019), 104874, https://doi.org/10.1016/j.compag.2019.104874.
- He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), 770–778.
- Tan, M., Le, Q., Efficientnet: Rethinking model scaling for convolutional neural networks, International conference on machine learning, (2019), 6105–6114.
- ISSN: 1303-6009
- Yayın Aralığı: Yılda 2 Sayı
- Başlangıç: 2019
- Yayıncı: Ankara Üniversitesi
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Classification of five different rice seeds grown in Turkey with deep learning methods