Open Access
Issue
E3S Web Conf.
Volume 661, 2025
The 18th Thai Society of Agricultural Engineering International Conference “Climate Resilient Agriculture for Asia” (TSAE 2025)
Article Number 03002
Number of page(s) 6
Section Computers and Electronics in Agricultural Engineering
DOI https://doi.org/10.1051/e3sconf/202566103002
Published online 13 November 2025
  1. Devkota, K. P., Devkota, M., Rezaei, M., Oosterbaan, R., Managing salinity for sustainable agricultural production in salt-affected soils of irrigated drylands. Agricultural Systems. 198, 103390 (2022). [Google Scholar]
  2. Metternicht, G. I., Zinck, J. A., Remote sensing of soil salinity: potentials and constraints. Remote sensing of environment. 85(1), 1–20 (2003). [Google Scholar]
  3. Cui, J., Chen, X., Han, W., Cui, X., Ma, W., Li, G., Estimation of soil salt content at different depths using uav multi-spectral remote sensing combined with machine learning algorithms. Remote Sensing. 15(21), 5254 (2023). [Google Scholar]
  4. Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., Shi, Z., Quantitative estimation of soil salinity using uav-borne hyperspectral and satellite multispectral images. Remote Sensing. 11(7), 736 (2019). [Google Scholar]
  5. Zhai, J., Wang, N., Hu, B., Han, J., Feng, C., Peng, J., Luo, D., Shi, Z., Estimation of soil salinity by combining spectral and texture information from uav multispectral images in the tarim river basin, china. Remote Sensing. 16(19), 3671 (2024). [Google Scholar]
  6. Zhao, W., Zhou, C., Zhou, C., Ma, H., Wang, Z., Soil salinity inversion model of oasis in arid area based on uav multispectral remote sensing. Remote Sensing. 14(8), 1804 (2022). [Google Scholar]
  7. Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Franceschini, M. H., Kramer, H., van Loo, E. N., Roman, V. J., Finkers, R., UAV based soil salinity assessment of cropland. Geoderma. 338, 502-512 (2019). [Google Scholar]
  8. Alava Bermeo, M., Garcia Solôrzano, A., Pacheco Gil, H., Model for estimating soil chemical properties with rgb drone images. Enfoque UTE. 15(4), 19-26 (2024). [Google Scholar]
  9. Xu, L., Ma, H., Wang, Z., Soil salinity and soil water content estimation using digital images in coastal field: a case study in yancheng city of jiangsu province, china. Chin. Geogr. Sci. 32(4), 676-685 (2022). https://doi.org/10.1007/s11769-022-1293-1. [Google Scholar]
  10. Yao, H., Qin, R., Chen, X., Unmanned aerial vehicle for remote sensing applications—a review. Remote sensing. 11(12), 1443 (2019). [Google Scholar]
  11. Ballester, C., Brinkhoff, J., Quayle, W. C., Hornbuckle, J., Monitoring the effects of water stress in cotton using the green red vegetation index and red edge ratio. Remote Sensing. 11(7), 873 (2019). [Google Scholar]
  12. Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., He, Y., Combining uav-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sensing. 10(9), 1484 (2018). [Google Scholar]
  13. Li, S., Yuan, F., Ata-UI-Karim, S. T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q., Combining color indices and textures of uav-based digital imagery for rice lai estimation. Remote Sensing. 11(15), 1763 (2019). [Google Scholar]

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