Abstract
This research studies the possibility of applying data mining methods to determine homogeneous management zones in fields sown with cereals. For the study, satellite images of two fields in the East Kazakhstan region were used, obtained by the Sentinel-2 satellite in different periods of time (images of the first field were obtained from May to September 2020, images of the second field – from May to August 2021). Based on these images, a dataset of seasonal NDVI values was formed for each field. Four different clustering algorithms were applied to each of the datasets, the clustering results were visualized and rasterized as color maps, which were then offered for comparison and verification by an expert agronomist. Based on the expert review, recommendations were formulated for determining zones of homogeneous management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Misra, G., Cawkwell, F., Wingler, A.: Status of phenological research using Sentinel-2 data: a review. Remote Sens. 12(17), 2760 (2020)
Zhang, T., et al.: Band selection in Sentinel-2 satellite for agriculture applications. In: 23rd International Conference on Automation and Computing (ICAC), pp. 1–6. IEEE (2017)
Ghosh, P., et al.: Assessing crop monitoring potential of Sentinel-2 in a spatio-temporal scale. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42, 227–231 (2018)
Carlson, T.N., Ripley, D.A.: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62(3), 241–252 (1997)
Naser, M.A., et al.: Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sens. 12(5), 824 (2020)
Romani, L.A.S., et al.: Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops. In: 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE (2011)
Marino, S., Alvino, A.: Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis. Eur. J. Remote Sens. 51(1), 266–275 (2018)
Whelan, C., Harrell, G., Wang, J.: Understanding the k-medians problem. In: Proceedings of the International Conference on Scientific Computing (CSC), p. 219. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2015)
Feldman, D., Schulman, L.J.: Data reduction for weighted and outlier-resistant clustering. In: Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1342–1354 (2012)
Giordani, P., Ferraro, M.B., Martella, F.: Hierarchical clustering. In: An Introduction to Clustering with R. BQAHB, vol. 1, pp. 9–73. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-0553-5_2
Irani, J., Pise, N., Phatak, M.: Clustering techniques and the similarity measures used in clustering: a survey. Int. J. Comput. Appl. 134(7), 9–14 (2016)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press (1996)
Steinhaus, H.: Sur la division des corps materials en parties. Bull. Acad. Polon. Sci., III, IV, 801–804 (1956)
Zhang, N., Wang, M., Wang, N.: Precision agriculture — a worldwide overview. Comput. Electron. Agric. 36(2–3), 113–132 (2002). https://doi.org/10.1016/s0168-1699(02)00096-0
Ali, J.: Role of precision farming in sustainable development of hill agriculture. In: National Seminar on Technological Interventions for Sustainable Hill Development, At GB Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India (2013)
Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14(5), 1559–1571 (2015)
Cardot, H., Cenac, P., Monnez, J.-M.: A fast and recursive algorithm for clustering large datasets with k-medians. Comput. Stat. Data Anal. 56, 1434–1449 (2012)
Acknowledgement
This study has been supported by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP09259379).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nugumanova, A., Maulit, A., Sutula, M. (2022). Clustering Analysis Applied to NDVI Maps to Delimit Management Zones for Grain Crops. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-21967-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21966-5
Online ISBN: 978-3-031-21967-2
eBook Packages: Computer ScienceComputer Science (R0)