Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 5, pp. 71-84

Decision making tools on precision farming technologies feasibility based on geostatistical analysis of remote sensing data

O.A. Mitrofanova 1 , E.P. Mitrofanov 1 , V.P. Yakushev 1 , V.V. Yakushev 1 , V.M. Bure 1, 2 , S.Yu. Blokhina 1 
1 Agrophysical Research Institute, Saint Petersburg, Россия
2 Saint Petersburg State University, Saint Petersburg, Russia
Accepted: 16.08.2023
DOI: 10.21046/2070-7401-2023-20-5-71-84
The possibility of achieving economic efficiency from the introduction of precision agriculture technologies requires the development of predictive methods to assess the prospects for implementation those technologies, taking into account specific conditions. The authors propose a toolkit to support a method for assessing the feasibility of applying precision agriculture technologies on a given agricultural territory on the basis of variogram analysis of remote sensing data. In order to make a decision, it is necessary to investigate statistical relationships of the spatial distribution of the values of the parameter under study in the agricultural field and assess the degree of within-field heterogeneity, on which the application of differentiated agro-technological operations depends. The paper considers one of the approaches to constructing a geostatistical module for conducting predictive computational experiments, which allows to automate all stages of solving the problem of assessing the prospects for using precision agriculture technologies for specific soil and climatic conditions of an agricultural producer. The toolkit was developed using specialized statistical programming language R, in addition to standard libraries gstat, e1071, lattice, sp, etc. Visualization of the prototype was carried out on the basis of the shiny package. In the future it is planned to refine the prototype module for implementation in a single service for the analysis of heterogeneous geospatial data by various new and modern methods in order to make management decisions.
Keywords: precision farming, remote sensing data, geostatistics, nugget to sill ratio, geostatistical module, R
Full text

References:

  1. Yakushev V. P., Bure V. M., Mitrofanova O. A. et al., Within-field variability estimation based on variogram analysis of satellite data for precision agriculture, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 114–122 (in Russian), https://doi.org/10.21046/2070-7401-2020-17-2-114-122.
  2. Yakushev V. P., Bure V. M., Mitrofanova O. A., Mitrofanov E. P., Blokhina S. Yu., The specifics of aerospace image processing to optimize geostatistical approaches to within-field variability estimation in precision agriculture, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 128–139 (in Russian), https://doi.org/10.21046/2070-7401-2021-18-4-128-139.
  3. Ahmad L., Mahdi S. S., Chapter 12. Feasibility and evaluation of precision farming, Satellite Farming, Switzerland: Springer Nature, 2018, pp. 149–166, https://doi.org/10.1007/978-3-030-03448-1_12.
  4. Budzko V., Medennikov V., Mathematical modeling of evaluating the effectiveness of using RSD data in precision farming, Procedia Computer Science, 2021, Vol. 190, pp. 122–129, https://doi.org/10.1016/j.procs.2021.06.015.
  5. Bullock D. S., Boerngen M., Ta H. et al., The data intensive farm management project: Changing agronomic research through on-farm experimentation, Agronomy J., 2019, Vol. 111, No. 6, pp. 2736–2746, https://doi.org/10.2134/agronj2019.03.0165.
  6. Carvalho P., Costa J., Automatic variogram model fitting of a variogram map based on the Fourier integral method, Computers and Geosciences, 2021, Vol. 156, Article 104891, http://doi.org/10.1016/j.cageo.2021.104891.
  7. Chamara N., Islam M. D., Bai G. F. et al., Ag-IoT for crop and environment monitoring: past, present, and future, Agricultural Systems, 2022, Vol. 203, Article. 103497, http://doi.org.10.1016/j.agsy.2022.103497.
  8. Chlingaryan A., Sukkarieh S., Whelan B., Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review, Computers and Electronics in Agriculture, 2018, Vol. 151, pp. 61–69, https://doi.org/10.1016/j.compag.2018.05.012.
  9. Dong Y., Fu Z., Peng Y. et al., Precision fertilization method of field crops based on the Wavelet-BP neural network in China, J. Cleaner Production, 2020, Vol. 246, Article 118735, https://doi.org/10.1016/j.jclepro.2019.118735.
  10. Galioto F., Raggi M., Viaggi D., Assessing the potential economic viability of precision irrigation: a theoretical analysis and pilot empirical evaluation, Water, 2017, Vol. 9, No. 12, pp. 990–1009, https://doi.org/10.3390/w9120990.
  11. Garg A., Sapkota A., Haghverdi A., SAMZ-Desert: A Satellite-based agricultural management zoning tool for the desert agriculture region of southern California, Computers and Electronics in Agriculture, 2022, Vol. 194, Article 106803, https://doi.org/10.1016/j.compag.2022.106803.
  12. Gavioli A., de Souza E. G., Bazzi C. L. et al., Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods, Biosystems Engineering, 2019, Vol. 181, pp. 86–102, https://doi.org/10.1016/j.biosystemseng.2019.02.019.
  13. Iakushev V. P., Bure V. M., Mitrofanova O. A., Mitrofanov E. P., On the issue of semivariograms constructing automation for precision agriculture problems, Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya, 2020, Vol. 16, No. 2, pp. 177–185, https://doi.org/10.21638/11701/spbu10.2020.209.
  14. Jiang R., Sanchez-Azofeifa A., Laakso K. et al., UAV-based partially sampling system for rapid NDVI mapping in the evaluation of rice nitrogen use efficiency, J. Cleaner Production, 2021, Vol. 289, Article 125705, https://doi.org/10.1016/j.jclepro.2020.125705.
  15. Kernecker M., Knierim A., Wurbs A. et al., Experience versus expectation: farmers’ perceptions of smart farming technologies for cropping systems across Europe, Precision Agriculture, 2020, Vol. 21, No. 1, pp. 34–50, https://doi.org/10.1007/s11119-019-09651-z.
  16. Li Z., Zhang X., Clarke K. C. et al., An automatic variogram modeling method with high reliability fitness and estimates, Computers and Geosciences, 2018, Vol. 120, pp. 48–59, https://doi.org/10.1016/j.cageo.2018.07.011.
  17. Loures L., Chamizo A., Ferreira P. et al., Assessing the effectiveness of precision agriculture management systems in Mediterranean small farms, Sustainability, 2020, Vol. 12, Article 3765, https://doi.org/10.3390/su12093765.
  18. Lowder S. K., Skoet J., Raney T., The number, size, and distribution of farms, smallholder farms, and family farms worldwide, World Development, 2016, Vol. 87, pp. 16–29, https://doi.org/10.1016/j. worlddev.2015.10.041.
  19. MacKie E. J., Field M., Wang L. et al., GStatSim V1.0: a Python package for geostatistical interpolation and simulation: preprint, EGUsphere, 2022, https://doi.org/10.5194/egusphere-2022-1224.
  20. Mizik T., How can precision farming work on a small scale? A systematic literature review, Precision Agriculture, 2023, Vol. 24, pp. 384–406, https://doi.org/10.1007/s11119-022-09934-y.
  21. Pathmudi V. R., Khatri N., Kumar S. et al., A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications, Scientific African, 2023, Vol. 19, Article e01577, http://doi.org/10.1016/j.sciaf.2023.e01577.
  22. Paul K., Chatterjee S. S., Pai P. et al., Viable smart sensors and their application in data driven agriculture, Computers and Electronics in Agriculture, 2022, Vol. 198, Article 107096, https://doi.org/10.1016/j.compag.2022.107096.
  23. Razavi S., Sheikholeslami R., Gupta H. V., Haghnegahdar A., VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis, Environmental Modelling and Software, 2019, Vol. 112, pp. 95–107, https://doi.org/10.1016/j.envsoft.2018.10.005.
  24. Rodrigues M. S., Castrignanò A., Belmonte A. et al., Geostatistics and its potential in Agriculture 4.0, Revista Ciência Agronomica, 2020, Vol. 51, Special Agriculture 4.0, Article e20207691, http://doi.org/10.5935/1806-6690.20200095.
  25. Singh P. K., Sharma A., An intelligent WSN-UAV-based IoT framework for precision agriculture application, Computers and Electrical Engineering, 2022, Vol. 100, Article 107912, http://doi.org./10.1016/j.compeleceng.2022.107912.
  26. The state of food and agriculture 2020. Overcoming water challenges in agriculture, FAO, Rome, 2020, 210 p., https://doi.org/10.4060/cb1447en.
  27. Vecchio Y., De Rosa M., Pauselli G. et al., The leading role of perception: the FACOPA model to comprehend innovation adoption, Agricultural and Food Economics, 2022, Vol. 10, No. 5, pp. 1–19, https://doi.org/10.1186/s40100-022-00211-0.
  28. Yakushev V., Petrushin A., Mitrofanova O. et al., Spatial distribution prediction of agro-ecological parameter using kriging, E3S Web of Conf. Topical Problems of Green Architecture, Civil and Environmental Engineering, TPACEE 2019, 2020, Article 06030, https://doi.org/10.1051/e3sconf/202016406030.
  29. Yamasaki Y., Morie M., Noguchi N., Development of a high-accuracy autonomous sensing system for a field scouting robot, Computers and Electronics in Agriculture, 2022, Vol. 193, Article 106630, https://doi.org/10.1016/j.compag.2021.106630.
  30. Yasojima C., Protázio J., Meiguins B. et al., A new methodology for automatic cluster-based kriging using K-nearest neighbor and Genetic algorithms, Information, 2019, Vol. 10, No. 11, Article 357, https://doi.org/10.3390/info10110357.