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Rotation-Gamma Correction augmentation on CNN-Dense block for soil image classification

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Soil is a solid particle that covers the surface of the earth. Soil can be classified based on its color because the color indicates the nature and condition of the soil. CNN works well for image classification, but it requires large amounts of data. Augmentation is a technique to increase the amount of training data with various transformation techniques to the existing data. Rotation and Gamma Correction can be used simply as an augmentation technique and can reproduce an image with as many image variations as desired from the original image. CNN architecture has a convolution layer and Dense block has dense layers. The addition of Dense blocks to CNN aims to overcome underfitting and overfitting problems. This study proposes a combination of Augmentation and classification. In augmentation, a combination of rotation and Gamma correction techniques is used to reproduce image data. The CNN-Dense block is applied for classification. The soil image classification is grouped based on 5 labels black soil, cinder soil, laterite soil, peat soil, and yellow soil. The performances of the proposed method provide excellent results, where accuracy, precision, recall, and F1-Score performances are above 90%. It can be concluded that the combination of rotation and Gamma Correction as augmentation techniques and CNN-Dense blocks is powerful for use in soil image classification.
Słowa kluczowe
Rocznik
Strony
96--115
Opis fizyczny
Bibliogr. 31 poz., fig., tab.
Twórcy
  • Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya
  • Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya
  • Biology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya
autor
  • Agriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya
  • Informatics Technique Departement, Politeknik Negeri Lhokseumawe, Lhokseumawa, Indonesia
autor
  • Soil Departement, Faculty of Agriculture, Universitas Sriwijaya
  • Research Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia
Bibliografia
  • [1] Abuqaddom, I., Mahafzah, B. A., & Faris, H. (2021). Oriented stochastic loss descent algorithm to train very deep multi-layer neural networks without vanishing gradients. Knowledge-Based Systems, 230, 107391. https://doi.org/https://doi.org/10.1016/j.knosys.2021.107391
  • [2] Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q., & Cai, K. (2020). A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management, 240, 106303. https://doi.org/10.1016/j.agwat.2020.106303
  • [3] Chen, W., Yang, B., Li, J., & Wang, J. (2020). An approach to detecting diabetic retinopathy based on integrated shallow convolutional neural networks. IEEE Access (vol. 8, pp. 178552–178562). IEEE. https://doi.org/10.1109/ACCESS.2020.3027794
  • [4] Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1800–1807). IEEE. https://doi.org/10.1109/CVPR.2017.195
  • [5] Desiani, A., Adrezo, M., Chika Marselina, N., Arhami, M., Salsabila, A., & Gibran Al-Filambany, M. (2022). A combination of image enhancement and U-Net architecture for segmentation in identifying brain tumors on CT-SCAN Images. 2022 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 423–428). IEEE. https://doi.org/10.1109/ICIMCIS56303.2022.10017519
  • [6] Desiani, A., Erwin, Maiyanti, S. I., Suprihatin, B., Rachmatullah, N., Fauza, A. N., & Ramayanti, I. (2022). R-peak detection of beat segmentation and convolution neural network for arrhythmia classification. Journal of Engineering Science and Technology (JESTEC), 17(2), 1231–1246.
  • [7] Desiani, A., Erwin, Suprihatin, B., Adrezo, M., & Alfan, A. M. (2021). A hybrid system for enhancement retinal image reduction. 2021 International Conference on Informatics, Multimedia, Cyber, and Information System, (ICIMCIS) (pp. 80–85). IEEE. https://doi.org/10.1109/ICIMCIS53775.2021.9699259
  • [8] Desiani, A., Erwin, Suprihatin, B., Efriliyanti, F., Arhami, M., & Setyaningsih, E. (2022). VG-DropDNet a robust architecture for blood vessels segmentation on retinal image. IEEE Access (vol. 10, pp. 92067-92083). IEEE. https://doi.org/10.1109/access.2022.3202890
  • [9] Desiani, A., Erwin, Suprihatin, B., Yahdin, S., Putri, A. I., & Husein, F. R. (2021). Bi-path Architecture of CNN Segmentation and classification method for cervical cancer disorders based on pap-smear images. IAENG International Journal of Computer Science, 48(3), 37.
  • [10] Erwin, Safmi, A., Desiani, A., Suprihatin, B., & Fathoni. (2022). The augmentation data of retina image for blood vessel segmentation using U-Net convolutional neural network method. International Journal of Computational Intelligence and Applications, 21(01), 2250004. https://doi.org/10.1142/S1469026822500043
  • [11] Hamwood, J., Alonso-Caneiro, D., Read, S. A., Vincent, S. J., & Collins, M. J. (2018). Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomedical Optics Express, 9(7), 3049–3066. https://doi.org/10.1364/boe.9.003049
  • [12] Hamzah, Diqi, M., & Ronaldo, A. D. (2021). Effective soil type classification using convolutional neural network. International Journal of Informatics and Computation, 3(1), 20–29. https://doi.org/10.35842/ijicom.v3i1.33
  • [13] Hang, J., Zhang, D., Chen, P., Zhang, J., & Wang, B. (2019). Classification of plant leaf diseases based on improved convolutional neural network. Sensors, 19(19), 4161. https://doi.org/10.3390/s19194161
  • [14] Harlianto, P. A., Adji, T. B., & Setiawan, N. A. (2017). Comparison of machine learning algorithms for soil type classification. 2017 3rd International Conference on Science and Technology - Computer (ICST) (pp. 7-10). IEEE. https://doi.org/10.1109/ICSTC.2017.8011843
  • [15] Hartemink, A. E., & Minasny, B. (2014). Towards digital soil morphometrics. Geoderma, 230-231, 305-317. https://doi.org/10.1016/j.geoderma.2014.03.008
  • [16] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90
  • [17] Huang, G., Liu, Z., Maaten, L. Van Der, & Weinberger, K. Q. (2017). Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2261–2269). IEEE. https://doi.org/10.1109/CVPR.2017.243
  • [18] Huang, G., Liu, Z., Pleiss, G., Maaten, L. van der, & Weinberger, K. Q. (2022). Convolutional networks with dense connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 8704–8716. https://doi.org/10.1109/TPAMI.2019.2918284
  • [19] Kalyani, N. L., & Prakash, K. B. (2022). Soil color as a measurement for estimation of fertility using deep learning techniques. International Journal of Advanced Computer Science and Applications, 13(5), 305–310. https://doi.org/10.14569/IJACSA.2022.0130536
  • [20] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25. Curran Associates.
  • [21] Lanjewar, M. G., & Gurav, O. L. (2022). Convolutional neural networks based classifications of soil images. Multimedia Tools and Applications, 81, 10313–10336. https://doi.org/10.1007/s11042-022-12200-y
  • [22] Novakovi, J. D., Veljovi´c, A., Ili´, S. S., Papic, Z., & Milica, T. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7 , 39–46.
  • [23] Rahman, S., Rahman, M. M., Abdullah-Al-Wadud, M., Al-Quaderi, G. D., & Shoyaib, M. (2016). An adaptive gamma correction for image enhancement. Eurasip Journal on Image and Video Processing, 35, 2016. https://doi.org/10.1186/s13640-016-0138-1
  • [24] Sharma, S., Sharma, S., & Athaiya, A. (2020). Activation functions in neural networks. International Journal of Engineering Applied Sciences and Technology, 4(12), 310–316. https://doi.org/10.33564/ijeast.2020.v04i12.054
  • [25] Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556
  • [26] Sun, X., Fang, H., Yang, Y., Zhu, D., Wang, L., Liu, J., & Xu, Y. (2021). Robust retinal vessel segmentation from a data augmentation perspective. In H. Fu, M. K. Garvin, T. MacGillivray, Y. Xu, & Y. Zheng (Eds.), Ophthalmic Medical Image Analysis (vol. 12970, pp. 189–198). Springer. https://doi.org/10.1007/978-3-030-87000-3_20
  • [27] Taher, K. I., Abdulazeez, A. M., & Zebari, D. A. (2021). Data mining classification algorithms for analyzing soil data. Asian Journal of Research in Computer Science, 8(2), 17–28. https://doi.org/10.9734/ajrcos/2021/v8i230196
  • [28] Thanapol, P., Lavangnananda, K., Bouvry, P., Pinel, F., & Leprévost, F. (2020). Reducing overfitting and improving generalization in training convolutional neural network (CNN) under limited sample sizes in image recognition. 2020 - 5th International Conference on Information Technology (InCIT) (pp. 300–305) IEEE. https://doi.org/10.1109/InCIT50588.2020.9310787
  • [29] Wang, M., & Deng, W. (2018). Deep visual domain adaptation: a survey. Neurocomputing, 312, 135–153. https://doi.org/10.1016/j.neucom.2018.05.083
  • [30] Wu, C., Zou, Y., & Zhan, J. (2019). DA-U-Net: Densely connected convolutional networks and decoder with attention gate for retinal vessel segmentation. IOP Conference Series: Materials Science and Engineering, 533, 012053 . https://doi.org/10.1088/1757-899X/533/1/012053
  • [31] Yu, H., Zou, W., Chen, J., Chen, H., Yu, Z., Huang, J., Tang, H., Wei, X., & Gao, B. (2019). Biochar amendment improves crop production in problem soils : A review. Journal of Environmental Management, 232, 8–21. https://doi.org/10.1016/j.jenvman.2018.10.117
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-36bcbd6e-3348-40c4-9c7b-5cbe6fe2bd40
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