Implementation of the Vision Transformer (ViT) Method in Soil Type Classification
DOI:
https://doi.org/10.51903/zqs2q531Keywords:
Vision Transformer, soil classification, deep learningAbstract
This study applies the Vision Transformer (ViT) method to soil-type classification and evaluates its accuracy using digital images. The Vision Transformer (ViT) is a Deep Learning architecture that uses self-attention to extract global features from images, enabling it to recognize texture and color patterns more comprehensively than other convolutional methods. The dataset used consists of eight soil types, each containing 77 image data in “.jpg” format. Each image was processed and augmented to increase the dataset to 700 images per soil type. This was done to prevent overfitting. The ViT model was trained using a 90%/10 % split of the training and validation datasets. The results show that the Vision Transformer (ViT) architecture achieves an accuracy of 71.4%, with a small difference between training and validation accuracy, indicating that the ViT method generalizes well. This method successfully distinguishes soil types such as alluvial, andosol, laterite, and limestone based on subtle visual differences. These results demonstrate that the Vision Transformer (ViT) is effective for soil type classification and has the potential to serve as a basis for developing a digital image-based soil type classification system to support research and precision agriculture.
References
[1] M. Lamsani, R. A. Pangestika, M. Cahyanti, and E. R. Swedia, “Sistem Identifikasi Warna Tanah Munsell Menggunakan Sensor Warna TCS3200 Dan Kelembaban YL-69,” Sebatik, vol. 27, no. 1, pp. 379–389, Jun. 2023, doi: 10.46984/sebatik.v27i1.2249.
[2] L. Lauer et al., “Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children,” Sensors, vol. 21, no. 19, Oct. 2021, doi: 10.3390/s21196623.
[3] M. Firdaus and R. Afwani, “Pengembangan Restful API untuk Aplikasi Klasifikasi Jenis Tanah Berbasis Mobile pada Google Cloud (Restful API Development for Mobile-Based Soil Type Classification Applications on Google Cloud),” Jurnal Teknologi Informasi, Komputer dan Aplikasinya, vol. 6, no. 1, pp. 275–287, 2024. [Online]. Available: http://jtika.if.unram.ac.id/index.php/jtika/275
[4] V. Birukov and V. Mostovoy, “Optimisation of Acrylic Fiber Temperature Regime Thermostabilisation with Computer Modelling,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 578–581. doi: 10.1016/j.procs.2020.02.207.
[5] Y. P. Astuti, I. Wardatunizza, E. R. Subhiyakto, and E. Kartikadarma, “Implementasi Algoritma Convolutional Neural Network (CNN) untuk Klasifikasi Jenis Tanah Berbasis Android,” Jurnal Informatika: Jurnal Pengembangan IT (JPIT), vol. 8, no. 3, pp. 220–225, sep. 2023, doi: 10.30591/jpit.v8i3.5026.
[6] R. Shalehuddin Albawani, F. Tri Anggraeny, and M. Muharrom Al Haromainy, “Implementasi SEBlock pada Klasifikasi Citra Penyakit Mata Manusia dengan Arsitektur MobileNetV3-Small,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 1123–1128, 2024, doi: 10.36040/jati.v8i1.8916.
[7] G. Ashari Rakhmat and M. Fikri Haekal, “MIND (Multimedia Artificial Intelligent Networking Database Peningkatan Performa MobilenetV3 dengan Squeeze-and-Excitation (Studi Kasus Klasifikasi Kesegaran Ikan Berdasarkan Mata Ikan),” Journal MIND Journal | ISSN, vol. 8, no. 1, pp. 27–41, 2023, doi: 10.26760/mindjournal.v8i1.27-41.
[8] Y. Kim, J. Park, J. Yoon, and J. Kim, “Improved Q Network Auto-Scaling in Microservice Architecture,” Applied Sciences (Switzerland), vol. 12, no. 3, p. 1206, Feb. 2022, doi: 10.3390/app12031206.
[9] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 12, pp. 6999–7019, Jun. 2021, doi: 10.1109/tnnls.2021.3084827.
[10] A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam, “Searching for MobileNetV3,” arXiv preprint, May 2019. doi:10.48550/arxiv.1905.02244.
[11] H. Syuhada, S. Hidayat, S. Mulyati, and A. Giri Persada, “Pengembangan Gamifikasi pada Pelajaran Matematika SD dengan Metode Addie untuk Meningkatkan Minat Belajar Siswa,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 1, pp. 1–14, Dec. 2023, doi: 10.36341/rabit.v9i1.466.
[12] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” arXiv preprint. doi:10.48550/arxiv.1905.11946.
[13] K. Naseer and H. N. Ahmed, “Effectiveness and Reliability of Artificial Intelligence in Fraud Detection: A Mixed-Method Study on Financial Audit,” Journal of Management and Informatics, vol. 4, no. 1, pp. 706–722, Apr. 2025, doi: 10.51903/jmi.v4i1.168.
[14] A. Syafiq, M. Zuhri, N. Nafiiyah, and A. Setia Budi, “Identification of Road Damage Using the Convolutional Neural Network (CNN) Method,” Jurnal Ilmiah Sistem Informasi, vol. 4, no. 3, pp. 802–817, Nov. 2025, doi: 10.51903/jgn2sd35.
[15] W. L. Tan and A. Menon, “3D Medical Image Reconstruction through Transformer-Based Neural Networks: A Comparative Study,” Journal of Technology Informatics and Engineering, vol. 4, no. 3, pp. 448–462, Dec. 2025, doi: 10.51903/jtie.v4i3.453.
[16] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), pp. 10012–10022, oct. 2021, doi: 10.1109/iccv48922.2021.00986.
[17] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” arXiv preprint, Oct. 2020. doi:10.48550/arxiv.2010.11929.
[18] W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2021, pp. 568–578, doi: 10.1109/iccv48922.2021.00061.
[19] S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in Vision: A Survey,” ACM Computing Surveys, vol. 54, no. 10s, pp. 1–41, jan. 2022, doi: 10.1145/3505244.
[20] N. Park and S. Kim, “How Do Vision Transformers Work?,” arXiv preprint, Feb. 2022. doi: 10.48550/arxiv.2202.06709.
[21] J. Daud Halim and Risal, “Klasifikasi Sel Darah Putih Menggunakan Vision Transformer (ViT),” Jurnal Strategi: Jurnal Maranatha, vol. 6, no. 2, pp. 291–307, Nov. 2024. [Online]. Available: https://strategi.itmaranatha.org/index.php/strategi/article/view/521
[22] J. A. Figo, N. Yudistira, and A. W. Widodo, “Deteksi Covid 19 dari Citra X Ray Menggunakan Vision Transformer (ViT),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 1116–1125, mar. 2023. [Online]. Available: https://j ptiik.ub.ac.id/index.php/j ptiik/article/view/12388
[23] H. Bao, L. Dong, S. Piao, and F. Wei, “BEiT: BERT Pre Training of Image Transformers,” arXiv preprint, Jun. 2021. doi: 10.48550/arxiv.2106.08254.
[24] F. Sigloch, S. Sangiao, P. Orús, and J. M. de Teresa, “Large Output Voltage to Magnetic Flux Change in NanoSQUIDs Based on Direct Write Focused Ion Beam Induced Deposition Technique,” Nanoscale Advances, vol. 4, pp. 4628–4634, sep. 2022, doi: 10.1039/d2na00602b.
[25] L. O. Villegas, C. Moreno, M. A. Pajkos, M. Zanolin, and J. M. Antelis, “Parameter Estimation From the Core Bounce Phase of Rotating Core Collapse Supernovae in Real Interferometer Noise,” arXiv preprint, Apr. 2023. doi: 10.48550/arxiv.2304.01267.
[26] E. Parisotto, H. F. Song, J. W. Rae, R. Pascanu, C. Gulcehre, S. M. Jayakumar, M. Jaderberg, R. Lopez Kaufman, A. Clark, S. Noury, M. M. Botvinick, N. Heess, and R. Hadsell, “Stabilizing Transformers for Reinforcement Learning,” arXiv preprint, oct. 2019. doi: 10.48550/arxiv.1910.06764.
[27] J. W. Wei and K. Zou, “EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks,” in Proc. Empirical Methods in Natural Language Processing and 9th Int. Joint Conf. Natural Language Processing (EMNLP IJCNLP), Nov. 2019, pp. 6382–6388, doi: 10.18653/v1/d19 1670.
[28] T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 318–327, Feb. 2018, doi: 10.1109/tpami.2018.2858826.
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