ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 6, pp. 151-162

Automatic delineation algorithm for within-field variability zones based on aerospace images and optical criteria

V.P. Yakushev 1 , A.F. Petrushin 1 , V.V. Yakushev 1 , S.Yu. Blokhina 1 , Yu.I. Blokhin 1 , D.A. Matveenko 1 , E.P. Mitrofanov 1 
1 Agrophysical Research Institute, Saint Petersburg, Russia
Accepted: 28.11.2022
DOI: 10.21046/2070-7401-2022-19-6-151-162
Rational application of precision agriculture technologies is impossible without a quantitative assessment of the range of within-field variability of the crop development and yield formation factors on cultivated agricultural lands. For the variable rate site-specific management of crop growing, it is very important to evaluate the degree of within-field variability of those factors. Remote sensing has been considered to be the most efficient, scalable, and cost-effective way to quantify spatial variability of crop and soil properties. The software implementation of the basic algorithm for within-field variability delineation and border marking based on aerospace images and optical criteria of crop canopy is presented. A modular control scheme for the formation of a knowledge base, a database and the process of calculating optical indices according to specified criteria and satellite images of the studied agricultural areas has been developed. The software module for creating a database is equipped with a Pascal graphical interface in the Rad Studio 11 software environment and contains various tables and references background information. Based on these data, the knowledge base has been filling with the calculated values of various optical indices characterizing the physiological state of the studied crops in various phases of their development, indicating the range of acceptable and critical values of possible stress factors.
Keywords: precision agriculture, remote sensing, optical criteria of plants, nitrogen and water deficiency, within-field variability, delineation algorithm
Full text


  1. Denisov P. V., Sereda I. I., Troshko K. A., Loupian E. A., Plotnikov D. E., Tolpin V. A., Opportunities and experience of operational remote monitoring of winter crops condition in Russia, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 171–185 (in Russian), DOI: 10.21046/2070-7401-2021-18-2-171-185.
  2. Matveenko D. A., Voropaev V. V., Yakushev V. V., Blokhin Yu. I., Blokhia S. Yu., Mitrofanov E. P., Petrushin A. F., Current state and trends of developing new methods for quantitative assessment of within-field heterogeneity for precision agriculture, Agrofizika, 2020, No. 1, pp. 59–70 (in Russian), DOI: 10.25695/AGRPH.2020.01.09.
  3. Yakushev V. P., Dubenok N. N., Loupian E. A. (2019a), Earth remote sensing technologies for agriculture: application experience and development prospects, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 3, pp. 11–23 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-11-23.
  4. Yakushev V. P., Kanash E. V., Yakushev V. V., Matveenko D. A., Rusakov D. V., Blokhina S. Yu., Petrushin A. F., Mitrofanov E. P. (2019b), Advanced features of automated detection of within-field variability based on hyperspectral images and optical criteria, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 3, pp. 24–32 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-24-32.
  5. Yakushev V. P., Kanash E. V., Rusakov D. V., Yakushev V. V., Blokhina S. Yu., Petrushin A. F., Blokhin Yu. I., Mitrofanova O. A., Mitrofanov E. P., Correlation dependences between crop reflection indices, grain yield and optical characteristics of wheat leaves at different nitrogen level and seeding density, Sel’skokhozyaistvennaya biologiya, 2022, Vol. 57, No. 1, pp. 98–112 (in Russian), DOI: 10.15389/agrobiology.2022.1.98rus.
  6. Breunig F. M., Galvão L. S., Dalagnol R., Dauve C. E., Parraga A., Santi A. L., Della Flora D. P., Chen S., Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data, Intern. J. Applied Earth Observation Geoinformation, 2020, Vol. 85, Art. No. 102004, DOI: 10.1016/j.jag.2019.102004.
  7. Cammarano D., Zha H., Wilson L., Li Y., Batchelor W. D., Miao Y., A Remote sensing-based approach to management zone delineation in small scale farming systems, Agronomy, 2020, Vol. 10, No. 11, Art. No. 1767, DOI: 10.3390/agronomy10111767.
  8. Damian J. M., Santi A. L., Fornari M., Da Ros C. O., Eschner V. L., Monitoring variability in cash-crop yield caused by previous cultivation of a cover crop under a no-tillage system, Computers and Electronics in Agriculture, 2017, Vol. 142, pp. 607–621, DOI: 10.1016/j.compag.2017.11.006.
  9. Damian J. M., de Castro Pias O. H., Cherubin M. R., de Fonseca A. Z., Fornari E. Z., Santi A. L., Applying the NDVI from satellite images in delimiting management zones for annual crops, Scientia Agricola, 2020, Vol. 77, No. 1, Art. No. e20180055, DOI: 10.1590/1678-992x-2018-0055.
  10. De Benedetto D., Castrignano A., Rinaldi M., Ruggieri S., Santoro F., Figorito B., Gualano S., Diacono M., Tamborrino R., An approach for delineating homogeneous zones by using multi-sensor data, Geoderma, 2013, Vol. 199, pp. 117–127, DOI: 10.1016/j.geoderma.2012.08.028.
  11. Derby N. E., Casey F. X. M., Franzen D. W., Comparison of nitrogen management zone delineation methods for corn grain yield, Agronomy J., 2007, Vol. 99, pp. 405–414, DOI: 10.2134/agronj2006.0027.
  12. Easterday K., Kislik C., Dawson T. E., Hogan S., Kelly M., Remotely sensed water limitation in vegetation: insights from an experiment with unmanned aerial vehicles (UAVs), Remote Sensing, 2019, Vol. 11, No. 16, Art. No. 1853, DOI: 10.3390/rs11161853.
  13. Fontanet M., Scudiero E., Skaggs T. H., Fernàndez-Garcia D., Ferrer F., Rodrigo G., Bellvert J., Dynamic management zones for irrigation scheduling, Agricultural Water Management, 2020, Vol. 238, Art. No. 106207, DOI: 10.1016/j.agwat.2020.106207.
  14. Fridgen J. J., Kitchen N. R., Sudduth K. A., Drummond S. T., Wiebold W. J., Fraisse C. W., Management Zone Analyst (MZA), Agronomy J., 2004, Vol. 96, No. 1, pp. 100–108.
  15. 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, Art. No. 106803, DOI: 10.1016/j.compag.2022.106803.
  16. Gavioli A., de Souza E. G., Bazzi C. L., Guedes L. P. C., Schenatto K., Optimization of management zone delineation by using spatial principal components, Computers and Electronics in Agriculture, 2016, Vol. 127, pp. 302–310, DOI: 10.1016/j.compag.2016.06.029.
  17. Gavioli A., de Souza E. G., Bazzi C. L., Schenatto K., Betzek N. M., Identification of management zones in precision agriculture: an evaluation of alternative cluster analysis methods, Biosystems Engineering, 2019, Vol. 181, pp. 86–102, DOI: 10.1016/j.biosystemseng.2019.02.019.
  18. Georgi C., Spengler D., Itzerott S., Kleinschmit B., Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data, Precision Agriculture, 2018, Vol. 19, pp. 684–707, DOI: 10.1007/s11119-017-9549-y.
  19. Gili A., Alvarez C., Bagnato R., Noellemeyer E., Comparison of three methods for delineating management zones for site-specific crop management, Computers and Electronics in Agriculture, 2017, Vol. 139, pp. 213–223, DOI: 10.1016/j.compag.2017.05.022.
  20. Gu C., Wang X., Wang X., Yang F., Zhai C., Research progress on variable-rate spraying technology in orchards, Applied Engineering in Agriculture, 2020, Vol. 36, No. 6, pp. 927–942, DOI: 10.13031/aea.14201.
  21. Haghverdi A., Leib B. G., Washington-Allen R. A., Ayers P. D., Buschermohle M. J., Perspectives on delineating management zones for variable rate irrigation, Computers and Electronics in Agriculture, 2015, Vol. 117, pp. 154–167.
  22. Hong M.U., Bremer D. J., van der Merwe D., Using small unmanned aircraft systems for early detection of drought stress in Turfgrass, Crop Science, 2019, Vol. 59, No. 6, pp. 2829–2844.
  23. Jin Z., Prasad R., Shriver J., Zhuang Q., Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system, Precision Agriculture, 2017, Vol. 18, pp. 779–800, DOI: 10.1007/s11119-016-9488-z.
  24. Karydas C., Iatrou M., Iatrou G., Mourelatos S., Management zone delineation for site-specific fertilization in rice crop using multi-temporal RapidEye imagery, Remote Sensing, 2020, Vol. 12, Art. No. 2604, DOI: 10.3390/rs12162604.
  25. Khan H., Farooque A. A., Acharya B., Abbas F., Esau T. J., Zaman Q. U., Delineation of management zones for site-specific information about soil fertility characteristics through proximal sensing of potato fields, Agronomy, 2020, Vol. 10, Art. No. 1854, DOI: 10.3390/agronomy10121854.
  26. Lark R. M., Stafford J. V., Classification as a first step in the interpretation of temporal and spatial variation of crop yield, Annals of Applied Biology, 1997, Vol. 130, No. 1, pp. 111–121, DOI: 10.1111/j.1744-7348.1997.tb05787.x.
  27. Lu B., Dao P. D., Liu J., He Y., Shang J., Recent advances of hyperspectral imaging technology and applications in agriculture, Remote Sensing, 2020, Vol. 12, No. 16, Art. No. 2659, DOI: 10.3390/rs12162659.
  28. Mulla D. J., Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps, Biosystems Engineering, 2013, Vol. 114, pp. 358–371, DOI: 10.1016/j.biosystemseng.2012.08.009.
  29. Rossi R., Pollice A., Bitella G., Labella R., Bochicchio R., Amato M., Modelling the non-linear relationship between soil resistivity and alfalfa NDVI: a basis for management zone delineation, J. Applied Geophysics, 2018, Vol. 159, pp. 146–156, DOI: 10.1016/j.jappgeo.2018.08.008.
  30. Santos R. T., Saraiva A. M., A reference process for management zones delineation in precision agriculture, IEEE Latin America Transactions, 2015, Vol. 13, pp. 727–738, DOI: 10.1109/TLA.2015.7069098.
  31. Shaddad S. M., Madrau S., Castrignano A., Mouazen A. M., Data fusion techniques for delineation of site-specific management zones in a field in UK, Precision Agriculture, 2016, Vol. 17, No. 2, pp. 200–217, DOI: 10.1007/s11119-015-9417-6.
  32. Yao R.-J., Yang J.-S., Zhang T.-J., Gao P., Wang X.-P., Hong L.-Z., Wang M.-W., Determination of site-specific management zones using soil physico-chemical properties and crop yields in coastal reclaimed farmland, Geoderma, 2014, Vol. 232–234, pp. 381–393, DOI: 10.1016/j.geoderma.2014.06.006.