ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION
Sri INDRA MAIYANTI
Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya (Indonesia)
https://orcid.org/0009-0009-9983-8279
Anita DESIANI
anita_desiani@unsri.ac.idMathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya (Indonesia)
https://orcid.org/0000-0001-8851-2454
Syafrina LAMIN
Biology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya (Indonesia)
P PUSPITAHATI
Agriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya (Indonesia)
Muhammad ARHAMI
Informatics Technique Departement, Politeknik Negeri Lhokseumawe (Indonesia)
Nuni GOFAR
Soil Departement, Faculty of Agriculture, Universitas Sriwijaya (Indonesia)
Destika CAHYANA
Research Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia (Indonesia)
https://orcid.org/0000-0001-8461-0700
Abstract
Soil is a solid-particle that covers the earth's surface. Soils can be classified based their color. The color can be an indication of soil properties and soil conditions. Soil image classification requires high accuracy and caution. CNN works well on image classification, but CNN requires a large amount of data. Augmentation is one technique to overcome data needs like rotation and improving contrast. Rotation is the movement of rotating the image position randomly to various degrees. Gamma Correction is a method to improve image by decreasing or increasing the contrast. The rotation and Gamma Correction on augmentation can increase the amount of training data from 156 to 2500 soil images data. The classification of soil data is not referred to soil taxonomy system such as Entisols and Histosols but it used arbitrary simple classification based on color. Unfortunately, the weakness of the CNN is vanishing and exploded gradients. Another Deep learning that can overcome vanishing and exploded gradients is dense blocks. This study proposes a combination of Augmentation and CNN-Dense block where in the augmentation a combination of rotation and Gamma-correction techniques is used and Soil image classification based on color is used by the CNN-Dense block. The combination method is able to give excellent results, where all performances accuracy, precisions, recall and F1-Score are above 90%. The combination of rotation and Gamma Correction on augmentation and CNN is a robust method to use in soil image classification based on color.
Supporting Agencies
Keywords:
CNN, Image, Classification, Gamma Correction, Rotation, SoilReferences
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
DOI: https://doi.org/10.1016/j.knosys.2021.107391
Google Scholar
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
DOI: https://doi.org/10.1016/j.agwat.2020.106303
Google Scholar
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
DOI: https://doi.org/10.1109/ACCESS.2020.3027794
Google Scholar
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
DOI: https://doi.org/10.1109/CVPR.2017.195
Google Scholar
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
DOI: https://doi.org/10.1109/ICIMCIS56303.2022.10017519
Google Scholar
Desiani, A., Erwin, Maiyanti, S. I., Suprihatin, B., Rachmatullah, N., Fauza, A. N., & Ramayanti, I. (2022). Rpeak detection of beat segmentation and convolution neural network for arrhythmia classification. Journal of Engineering Science and Technology (JESTEC), 17(2), 1231–1246.
Google Scholar
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
DOI: https://doi.org/10.1109/ICIMCIS53775.2021.9699259
Google Scholar
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
DOI: https://doi.org/10.1109/ACCESS.2022.3202890
Google Scholar
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.
Google Scholar
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
DOI: https://doi.org/10.1142/S1469026822500043
Google Scholar
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
DOI: https://doi.org/10.1364/BOE.9.003049
Google Scholar
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
DOI: https://doi.org/10.35842/ijicom.v3i1.33
Google Scholar
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
DOI: https://doi.org/10.3390/s19194161
Google Scholar
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
DOI: https://doi.org/10.1109/ICSTC.2017.8011843
Google Scholar
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
DOI: https://doi.org/10.1016/j.geoderma.2014.03.008
Google Scholar
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
DOI: https://doi.org/10.1109/CVPR.2016.90
Google Scholar
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
DOI: https://doi.org/10.1109/CVPR.2017.243
Google Scholar
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
DOI: https://doi.org/10.1109/TPAMI.2019.2918284
Google Scholar
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
DOI: https://doi.org/10.14569/IJACSA.2022.0130536
Google Scholar
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.
Google Scholar
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
DOI: https://doi.org/10.1007/s11042-022-12200-y
Google Scholar
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.
Google Scholar
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
DOI: https://doi.org/10.1186/s13640-016-0138-1
Google Scholar
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
DOI: https://doi.org/10.33564/IJEAST.2020.v04i12.054
Google Scholar
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-030-87000-3_20
Google Scholar
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
DOI: https://doi.org/10.9734/ajrcos/2021/v8i230196
Google Scholar
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
DOI: https://doi.org/10.1109/InCIT50588.2020.9310787
Google Scholar
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
DOI: https://doi.org/10.1016/j.neucom.2018.05.083
Google Scholar
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
DOI: https://doi.org/10.1088/1757-899X/533/1/012053
Google Scholar
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
DOI: https://doi.org/10.1016/j.jenvman.2018.10.117
Google Scholar
Authors
Sri INDRA MAIYANTIMathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya Indonesia
https://orcid.org/0009-0009-9983-8279
Authors
Anita DESIANIanita_desiani@unsri.ac.id
Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya Indonesia
https://orcid.org/0000-0001-8851-2454
Authors
Syafrina LAMINBiology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya Indonesia
Authors
P PUSPITAHATIAgriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya Indonesia
Authors
Muhammad ARHAMIInformatics Technique Departement, Politeknik Negeri Lhokseumawe Indonesia
Authors
Nuni GOFARSoil Departement, Faculty of Agriculture, Universitas Sriwijaya Indonesia
Authors
Destika CAHYANAResearch Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia Indonesia
https://orcid.org/0000-0001-8461-0700
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