Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 71-84
Control of the state of agrocenoses based on Earth remote sensing data
I.M. Mikhailenko
1 , V.N. Timoshin
1 1 Agrophysical Research Institute, Saint Petersburg, Russia
Accepted: 01.06.2023
DOI: 10.21046/2070-7401-2023-20-3-71-84
The purpose of this work is to present new results of using Earth remote sensing data in the problem of managing agricultural technology in real time. The main reason for the low efficiency of modern precision farming technologies is the lack of an adequate theory of agricultural technology management. At the same time, when creating such a theory, one should take into account the fact that the object of management, which is agricultural technology, includes agrocenoses, in which, in addition to sowing a crop, weeds are also included. Failure to take this factor into account leads to a deterioration in management efficiency, a decrease in sowing productivity and an over expenditure of mineral fertilizers and herbicides. In the presented work, for the first time, a complete theory of managing the state of agrocenoses is presented. This theory makes it possible to obtain a given yield with the required reliability. Such management is formed on the basis of estimates of the parameters of the state of sowing crops and weeds, formed according to remote sensing data in real time. The presented theory is based on new mathematical models of parameters of the state of agricultural crops, soil environment, weeds, as well as models of the relationship of these parameters with remote sensing data. Control factors in agricultural technology are mineral fertilizers, herbicides and irrigation. Naturally, the parameters of technological operations are the doses of applied mineral fertilizers and herbicides, as well as irrigation rates. These operations are carried out at the onset of certain phenological phases of sowing crops. Remote sensing data are entered precisely at such moments of time when the parameters of the state of crops and weeds are estimated on their basis. The presented theory is based on classical control principles used in modern dynamic systems. According to the proposed theory, a specialized software package was developed, with the help of which the control system was tested on the example of spring wheat sowing.
Keywords: agricultural technologies, agrocenoses, state parameters, crop sowing, weeds, parameter estimation, control, real time
Full textReferences:
- Antonov V., Sladkih L., Crop monitoring and spring wheat yields forecasting basing on remote sensing data, Geomatika, 2009, No. 4, pp. 50–53 (in Russian).
- Bartalev S. A., Loupian E. A., Neishtadt I. A., Savin I. Yu., Gropland area classification in south regions of russia using MODIS satellite data, Issledovanie Zemli iz kosmosa, 2006, No. 3, pp. 68–75 (in Russian).
- Emelyanov Yu. Ya., Kopylov E. V., Kirillova E. V., Efficiency of herbicides in combination with fertilizers on spring wheat, Nivy Zaural’ya, 2013, No. 6(106), pp. 76–77 (in Russian).
- Kazakov I. E., Metody optimizatsii stokhasticheskikh system (Methods for optimizing stochastic systems), Moscow: Science, 1987, 484 p. (in Russian).
- Korsakov K. V., Strizhkov N. I., Pronko V. V., Combined application of fertilizers, herbicides and plant growth regulators in oat and millet in the Volga region, Vestnik Alta’skogo gosudarstvennogo agrarnogo universiteta, 2013, No. 4(102), pp. 16–19 (in Russian).
- Kochubey S. M., Shadchina T. M., Kobets N. I., Spektral’nye svoistva rastenii kak osnova distantsionnykh metodov diagnostiki (Spectral properties of plants as a basis for remote diagnostic methods), Kyiv: Science thought, 1990, 134 p. (in Russian).
- Marchukov V. S., Teoriya i metody tematicheskoi obrabotki aerokosmicheskikh izobrazhenii na osnove mnogourovnevoi segmentatsii: Diss. dokt. tekhn. nauk (Theory and methods of thematic processing of aerospace images based on multilevel segmentation, Dr. techn. sci. thesis), Moscow: 2011, 42 p. (in Russian).
- Mikhailenko I. M., Teoreticheskie osnovy i tekhnicheskaya realizatsiya upravleniya agrotekhnologiyami (Theoretical foundations and technical implementation of agricultural technology management), Saint Petersburg: SPbGTU, 2017, 250 p. (in Russian).
- Mikhailenko I. M., Timoshin V. N., Estimation of the chemical state of the soil environment according to the data of remote sensing of the Earth, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 18, No. 4, pp. 125–134 (in Russian), DOI: 10.21046/2070-7401-2018-15-7-102-113.
- Mikhailenko I. M., Timoshin V. N., Software management of soil fertility parameters under spring wheat crops, Agrochemistry, 2020, No. 8, pp. 86–93 (in Russian), DOI: 10.31857/S0002188120080062.
- Mikhailenko I. M., Timoshin V. N., Estimation of parameters of agrocenoses according to the data of remote sensing of the Earth, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 102–114 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-102-114.
- Mikhailenko I. M., Timoshin V. N., Program level of agrocenosis management, taking into account the impact of weeds on crops, Agricultural biology, 2022, Vol. 57, No. 3, pp. 500–517 (in Russian), DOI: 10.15389/agrobiology.2022.3.500rus.
- Nemchenko V. V., Rybina L. D., Gilev S. D., Kungurtseva N. M., Stepnykh N. V., Kopylov A. N., Kopylova S. V., Sovremennye sredstva zashchity rastenii i tekhnologii ikh primeneniya (Modern plant protection products and technologies for their application), Kurtamysh, 2006, 348 p. (in Russian).
- Rachkulik V. I., Sitnikova M. V., Otrazhatel’nye svoistva i sostoyanie rastitel’nogo pokrova (Reflective properties and condition of vegetation cover), Leningrad: Gidrometeoizdat, 1981, 287 p. (in Russian).
- Crippen R. E., Calculating the Vegetation Index Faster, Remote Sensing of Environment, 1990, Vol. 34, pp. 71–73, DOI: 10.1016/0034-4257(90)90085-Z.
- Datt B., A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves, J. Plant Physiology, 1999, Vol. 1, pp. 30–36, DOI: 10.1016/S0176-1617(99)80314-9.
- Derby N. E., Casey F. X. M., Franzen D. E., Comparison of nitrogen management zone delineation methods for corn grain yield, Agronomy J., 2007, Vol. 99, pp. 405–414, DOI: 10.2134//AGRONG.2006.0027.
- Gamon J. A., Serrano L., Surfus J. S., The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species. Functional Types and Nutrient Levels, Ecologia, 1997, Vol. 4, pp. 492–501, DOI: 10.1007/s004420050337.
- Heatherly L. G., Elmore T. W., Managing inputs for peak production, In: Soybeans: Improvement, Production and Uses, J. E. Specht, H. R. Boerma (eds.), Madison: ASA-CSSA-SSSA, 2004, pp. 451–536, DOI: 10.2134/agronmonogr16.3ed.c10.
- Jouven M., Carrère P., Baumont R., Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures, 1. Model description, Grass and Forage Science, 2006, Vol. 61, Issue 2, pp. 112–124, https://doi.org/10.1111/j.1365-2494.2006.00517.x.
- Kim K., Chavas J. P., Technological change and risk management: An application to the economics of corn production, Agricultural Economics, 2003, Vol. 29, pp. 125–142, DOI: 10.1016/S0169-5150(03)00081-1.
- Mikhailenko I. M. (2013a), Assessment of crop and soil state using satellite remote sensing data, Intern. J. Information Technology and Operations Management, 2013, Vol. 1, No. 5, pp. 41–52.
- Mikhailenko I. M. (2013b), Control of crop state using remote sensing information, Intern. J. Mathematical Modeling and Applied Computing, 2013, Vol. 1, No. 5, pp. 18–25.
- Mikhailenko I. M., Estimation of Parameters of Biomass State of Sowing Spring Wheat, Remote Sensing, 2022, Vol. 14, Issue 6, Art. No. 1388, https://doi.org/10.3390/rs14061388.
- Mikhailenko I. M., Timoshin V. N., Development of a methodology for assessing the parameters of the state of crops and soil environment for crops according to remote sensing of the Earth, IOP Conf. Series: Earth and Environmental Science, 2020, No. 548, Art. No. 052027, DOI: 10.1088/1755-1315/548/5/052027.
- Roudier P., Tisseyre B., Poilve H., Roger J.-M., A technical opportunity index adapted to zone-specific management, Precision Agriculture, 2011, Vol. 12, pp. 130–145, DOI: 10.1007/s11119-010-9160-y.
- Sami K., Kushal KC., John P. F., Scott S., Erdal O., Remote Sensing in Agriculture – Accomplishments, Limitations, and Opportunities, Remote Sensing, 2020, Vol. 12(22), Art. No. 3783, https://doi.org/10.3390/rs12223783.
- Sanderson M. A., Rotz C. A., Fultz S. W., Rauburn E. B., Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler, Agronomy J., 2001, Vol. 93, pp. 1281–1286, https://doi.org/10.2134/agronj2001.
- Sims D. A., Gamon J. A., Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages, Remote Sensing of Environment, 2002, pp. 337–354, DOI: 10.10.16/S0034-4257(02)00010-X.
- Steven M., Satellite remote sensing for agricultural management: Opportunities and logistic constraints ISPRS, J. Photogrammetry and Remote Sensing, 1993, Vol. 48, pp. 29–34, DOI: 10.1016/0924-2716(93)90029-M.
- Thompson J., Krogh P. H., A qualitative multi-attribute model for assessing the impact of cropping systems on soil quality, Pedobiologia, 2007, Vol 51(3), pp. 239–250, DPI: 10.1016/j.pedobi.2007.03.006.
- Tisseyre B., McBratney A. B., A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture, Precision Agriculture, 2008, Vol. 9, pp. 101–113, DOI: 10.1007/s11119-008-9053-5.