Identification of factors that negatively affect the growth of agricultural crops by methods of orthogonal transformations

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.257431

Keywords:

image processing, aerospace images, NDVI, SBC, orthogonal transformations, conceptual model

Abstract

This paper focuses on aerospace image analysis methods. Aerospace images are considered for the study of agricultural crops of northern Kazakhstan belonging to the A. I. Barayev Research and Production Center for Grain Farming. The main goal of the research is the development and implementation of algorithms that make it possible to detect and highlight on aerospace images the factors that negatively affect the growth of crops over the growing seasons. To resolve the problem, the spectral brightness coefficient (SBC), NDVI, clustering, orthogonal transformations are used. Special attention was paid to the development of software tools for selecting characteristics that describe texture differences to segment texture regions into sub-regions. That is, the issue of the applicability of sets of textural features and orthogonal transformations for the analysis of experimental data to identify characteristic areas on aerospace images that can be associated with weeds, pests, etc. in the future was investigated. The questions of signal image processing remain the focus of attention of different specialists. The images act both as a result and as a research object in physics, astronautics, meteorology, forensic medicine and many other areas of science and technology. Furthermore, image processing systems are currently being used to resolve many applied problems.

A program has been implemented in the MATLAB environment that allows performing spectral transformations of six types: 1) cosine; 2) Hadamard of order 2n; 3) Hadamard of order n=p+1, p≡3 (mod4); 4) Haar; 5) slant; 6) Daubechies 4.

Analysis of the data obtained revealed the features of changes in the reflectivity of cultivated crops and weeds in certain periods of the growing season. The data obtained are of great importance for the validation of remote space observations using aerospace images

Author Biographies

Moldir Yessenova, L. N. Gumilyov Eurasian National University

Doctoral Student

Department of Information Systems

Gulzira Abdikerimova, L. N. Gumilyov Eurasian National University

PhD

Department of Information Systems

Aknur Adilova, M. Kh. Dulaty Taraz Regional University

Teacher

Department of Information Systems

Akbota Yerzhanova, S. Seifullin Kazakh Agrotechnical University

Teacher

Department of Technological Machines and Equipment

Nurbol Kakabayev, Ualikhanov University

Doktor PhD, Аssociate Professor

Department of Engineering Technology and Transport

Talgatbek Ayazbaev, International Taraz Innovative Institute

Candidate of Physiko-Mathematical Sciences, Associate Professor

Department of Information and Communication Technologies

Zeinigul Sattybaeva, Ualikhanov University

Аssociate Professor

Department of Agriculture of Bioresources

Tleugaisha Ospanova, L. N. Gumilyov Eurasian National University

Candidate of Technical Sciences

Department of Information Systems

References

  1. Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67 (5), 786–804. doi: https://doi.org/10.1109/proc.1979.11328
  2. Feodor, M., Natalya, R. (2017). Analysis of noise stability of strip-transformation. Bulletin of the Novosibirsk Computing Center. Series: Computing Science, 41. doi: https://doi.org/10.31144/bncc.cs.2542-1972.2017.n41.p41-54
  3. Xiao, B., Lu, G., Zhang, Y., Li, W., Wang, G. (2016). Lossless image compression based on integer Discrete Tchebichef Transform. Neurocomputing, 214, 587–593. doi: https://doi.org/10.1016/j.neucom.2016.06.050
  4. Rao, K. R., Yip, P. (1990). Discrete cosine transform: algorithms, advantages, applications. Academic Press. doi: https://doi.org/10.1016/c2009-0-22279-3
  5. Kostrov, B. V., Grigorenko, D. V., Ruchkin, V. N., Fulin, V. A. (2016). Theoretical aspects of aerospace image processing in quasi two-dimensional spectral space. MATEC Web of Conferences, 75, 03006. doi: https://doi.org/10.1051/matecconf/20167503006
  6. Abdikerimova, G. B., Murzin, F. A., Bychkov, A. L., Wei, X., Ryabchikova, E. I., Ayazbayev, T. (2019). The analysis of textural images on the basis of orthogonal transformations. Journal of Theoretical and Applied Information Technology, 97 (1), 15–22.
  7. Rashmi, S., Mandar, S. (2011). Textural Feature Based Image Classification Using Artificial Neural Network. Advances in Computing, Communication and Control, 62–69. doi: https://doi.org/10.1007/978-3-642-18440-6_8
  8. Sidorova, V. S. (2012). Hierarchical cluster algorithm for remote sensing data of earth. Pattern Recognition and Image Analysis, 22 (2), 373–379. doi: https://doi.org/10.1134/s1054661812020149
  9. Chaban, L. N., Berezina, K. V. (2018). Analiz informativnosti spektral'nykh i teksturnykh priznakov pri klassifikatsii rastitel'nosti po giperspektral'nym aerosnimkam. Geodeziya i aerofotosemka, 62 (1), 85–95.
  10. Umarani, C., Ganesan, L., Radhakrishnan, S. (2008). Combined statistical and structural approach for unsupervised texture classification. International Journal of Imaging and Engineering, 2 (1), 162–165. Available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.329.4239&rep=rep1&type=pdf
  11. Galerne, B., Gousseau, Y., Morel, J.-M. (2011). Random Phase Textures: Theory and Synthesis. IEEE Transactions on Image Processing, 20 (1), 257–267. doi: https://doi.org/10.1109/tip.2010.2052822
  12. Costa, A. F., Humpire-Mamani, G., Traina, A. J. M. (2012). An Efficient Algorithm for Fractal Analysis of Textures. 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images. doi: https://doi.org/10.1109/sibgrapi.2012.15
  13. Salomon, D. (2004). Compression of data, images and sound. Moscow: Technosphere, 368.
  14. Vilenkin, N. Y. (1971). Combinatorics. Academic Press. doi: https://doi.org/10.1016/c2013-0-11655-8
  15. Paley, R. E. A. C. (1933). On Orthogonal Matrices. Journal of Mathematics and Physics, 12 (1-4), 311–320. doi: https://doi.org/10.1002/sapm1933121311
  16. Lachowicz, P. (2015). Walsh–Hadamard Transform and Tests for Randomness of Financial Return-Series. Quant At Risk. Available at: https://quantatrisk.com/2015/04/07/walsh-hadamard-transform-python-tests-for-randomness-of-financial-return-series/
  17. Yorke, B. A., Beddard, G. S., Owen, R. L., Pearson, A. R. (2014). Time-resolved crystallography using the Hadamard transform. Nature Methods, 11 (11), 1131–1134. doi: https://doi.org/10.1038/nmeth.3139
  18. Lu, Y., Desmedt, Y. (2015). Walsh transforms and cryptographic applications in bias computing. Cryptography and Communications, 8 (3), 435–453. doi: https://doi.org/10.1007/s12095-015-0155-4
  19. Seberry, J., Balonin, N. A. (2017). Two infinite families of symmetric Hadamard matrices. Faculty of Engineering and Information Sciences - Papers: Part B. 782. Available at: https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1783&context=eispapers1
  20. Slomczynski, W., Szczepanek, A. (2017). Quantum Dynamical Entropy, Chaotic Unitaries and Complex Hadamard Matrices. IEEE Transactions on Information Theory, 63 (12), 7821–7831. doi: https://doi.org/10.1109/tit.2017.2751507
  21. Balasubramanian, K. (2021). Combinatorics, Big Data, Neural Network & AI for Medicinal Chemistry & Drug Administration. Letters in Drug Design & Discovery, 18 (10), 943–948. doi: https://doi.org/10.2174/1570180818666210719130052
  22. Abdikerimova, G., Bychkov, A., Xin, Y. W., Murzin, F. et. al. (2016). Algorithms and software for the analysis of disordering the structure of cellular walls. Bulletin of the Novosibirsk Computing Center. Series:Computer Science, (40). doi: https://doi.org/10.31144/bncc.cs.2542-1972.2016.n40.p1-14
  23. Osadchiy, A., Kamenev, A., Saharov, V., Chernyi, S. (2021). Signal Processing Algorithm Based on Discrete Wavelet Transform. Designs, 5 (3), 41. doi: https://doi.org/10.3390/designs5030041
  24. Abdiakhmetova, Z. M. (2017). Wavelet data processing in the problems of allocation in recovery well logging. Journal of Theoretical and Applied Information Technology, 95 (5), 1041. Available at: https://www.kaznu.kz/content/files/news/folder23320/2017%20%D0%A1%D0%BA%D0%BE%D0%BF%D1%83%D1%81%207Vol95No5.pdf
  25. Borisova, D., Kazaryan, M., Shakhramanyan, M., Nedkov, R., Richter, A., Stankova, N. (2017). Research of generalized wavelet transformations of Haar correctness in remote sensing of the Earth. Image and Signal Processing for Remote Sensing XXIII. doi: https://doi.org/10.1117/12.2278572

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Published

2022-06-30

How to Cite

Yessenova, M., Abdikerimova, G., Adilova, A., Yerzhanova, A., Kakabayev, N., Ayazbaev, T., Sattybaeva, Z., & Ospanova, T. (2022). Identification of factors that negatively affect the growth of agricultural crops by methods of orthogonal transformations . Eastern-European Journal of Enterprise Technologies, 3(2 (117), 39–47. https://doi.org/10.15587/1729-4061.2022.257431