vestiagИзвестия Национальной академии наук Беларуси. Серия аграрных наукProceedings of the National Academy of Sciences of Belarus. Agrarian Series1817-72041817-7239The Republican Unitary Enterprise Publishing House "Belaruskaya Navuka"10.29235/1817-7204-2021-59-2-186-197vestiag-562Research ArticleЗЕМЛЯРОБСТВА І РАСЛІНАВОДСТВAAGRICULTURE AND PLANT CULTIVATIONИспользование данных дистанционного зондирования, полученных с БЛА, для оценки продуктивности биомассы Silphium perfoliatumUse of remote sensing data obtained from UAVs to assess the biomass productivity of Silphium perfoliatumМыслываТ. Н.MyslyvaT. N.

Мыслыва Тамара Николаевна, доктор сельскохозяйственных наук, доцент, зав. кафедрой геодезии и фотограмметрии

ул. Мичурина, 5, 213407 Горки, Могилевская обл.

Tamara N. Myslyva, D. Sc. (Agriculture), Professor

5 Michurina Str., Gorki 213407, Mogilev Region

byrty41@yahoo.comШелютоБ. В.SheliutaB. V.

Шелюто Бронислава Васильевна, доктор сельскохозяйственных наук, профессор, профессор кафедры кормопроизводства и хранения продукции растениеводства

ул. Мичурина, 5, 213407 Горки, Могилевская обл.

Branislava V. Sheliuta, D. Sc. (Agriculture), Professor

5 Michurina Str., Gorki 213407, Mogilev Region

a.sheliuta@mail.ruНадточийП. П.NadtochyP. P.

Надточий Петр Петрович, доктор сельскохозяйственных наук, профессор, ведущий научный сотрудник

Киевское шоссе, 132, 10132 Житомир, Житомирская обл.

Petr P. Nadtochyj, D. Sc. (Agriculture), Associate Professor

132 Kiev highway, Zhytomyr, Zhytomyr Region 10132

pnadtochy@yahoo.comКуцаеваО. А.KutsayevaA. A.

Куцаева Олеся Алексеевна, старший преподаватель кафедры геодезии и фотограмметрии

ул. Мичурина, 5, 213407 Горки, Могилевская обл.

Alesia A. Kutsayeva, Senior Lecturer (Agriculture)

5 Michurina Str., Gorki 213407, Mogilev Region

alexa-1982@bk.ruБелорусская государственная сельскохозяйственная академияBelarusian State Agricultural AcademyИнститут сельского хозяйства Полесья Национальной академии аграрных наук УкраиныInstitute of Agriculture of Polesie NAAS of Ukraine202110052021592186197Copyright © Мыслыва Т.Н., Шелюто Б.В., Надточий П.П., Куцаева О.А., 20212021Мыслыва Т.Н., Шелюто Б.В., Надточий П.П., Куцаева О.А.Myslyva T.N., Sheliuta B.V., Nadtochy P.P., Kutsayeva A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestiagr.belnauka.by/jour/article/view/562

Агромониторинг является одним из важнейших источников получения актуальной и оперативной информации о состоянии сельскохозяйственных культур. Ускорить и удешевить процесс его проведения возможно посредством использования данных дистанционного зондирования (ДДЗ), получаемых с помощью беспилотных летательных аппаратов (БЛА). Оценка возможности использования ДЗЗ сверхвысокого разрешения для определения продуктивности биомассы Silphium perfoliatum выполнялась с использованием БЛА Phantom-4ProV 2.0. Съемку проводили в режиме RGB, высота съемки – 50 м, пространственное разрешение – 2,5 см. По результатам съемки создавались карта высот и ортомозаика, используемые в дальнейшем для оценки продуктивности растений. Для получения значений высоты растений находили разницу между высотами растительного покрова, полученными из растра модели поверхности, и минимальной высотой, определенной в пределах растра. Фактическую высоту растений, измеренную в полевых условиях, сравнивали с данными, полученными с помощью БЛА, затем определяли продуктивность биомассы, рассчитанную по фактической и прогнозной высотам. Коэффициент детерминации для уравнения парной линейной регрессии между фактическим и прогнозным значениями продуктивности составил 0,97, а величина средней ошибки аппроксимации – 3,3 %. Для верификации полученных результатов в пределах территории исследования в полевых условиях отбирали 60 образцов биомассы, длину растений в которых определяли с помощью рулетки, а места отбора образцов координировали с помощью GPS-позиционирования. По откалиброванной ортомозаике на пиксельной основе по нормализированным RGB-каналам определяли 13 вегетационных индексов, из которых четыре (ExG, VARI, WI и EXGR) оказались пригодными для создания прогнозной модели множественной линейной регрессии, позволяющей осуществлять оценку и прогноз продуктивности биомассы Silphium perfoliatum в фазу стеблевания с ошибкой, не превышающей 2 %. Результаты исследования могут быть полезны как при разработке методики прогнозирования, так и при непосредственном прогнозировании продуктивности биомассы Silphium perfoliatum и других кормовых культур, в частности Helianthus annuus и Helianthus tuberosus.

Agromonitoring is one of the most important sources of obtaining up-to-date and timely information about the state of agricultural crops. It is possible to speed up and reduce the cost of its implementation process using remote sensing data (RSD) obtained with the help of unmanned aerial vehicles (UAVs). Possibility of using ultra-high-resolution remote sensing to determine productivity of Silphium perfoliatum biomass has been evaluated using Phantom-4ProV 2.0 UAV. The shooting was carried out in RGB mode, the shooting height was 50 m, the spatial resolution was 2.5 cm. Based on the results of the survey, a height map and orthomosaic were created, which were later used to assess productivity of plants. To obtain the plant height values, the difference between the vegetation cover heights obtained from the surface model raster and the minimum height determined within the raster has been calculated. The actual height of plants measured in the field was compared with the data obtained using the UAV, and after the biomass productivity calculated from the actual and predicted heights was determined. The determination coefficient for equation of paired linear regression between the actual and predicted values of productivity made 0.97, and the value of the average approximation error was 3.3 %. To verify the results obtained, 60 samples of biomass were taken in the field within the study area, with the length of the plants determined using a tape measure, and the sampling sites coordinated using GPS positioning. 13 vegetation indices have been determined using pixel-based calibrated orthomosaic and normalized RGB channels, four of which (ExG, VARI, WI, and EXGR) showed to be suitable for creating a predictive model of multiple linear regression, which allows estimating and predicting the productivity of Silphium perfoliatum biomass during stemming phase with an error not exceeding 2 %. The results of the study can be useful both in development of prediction methods and in the direct prediction of Silphium perfoliatum biomass and other forage crops productivity, in particular Helianthus annuus and Helianthus tuberosus.

агромониторингсельскохозяйственные культурымоделированиепрогнозированиерегрессионные моделидистанционное зондированиеБЛАвегетационный индекспродуктивностьбиомассаSilphium perfoliatumagromonitoringcropssimulationpredictionregression modelsremote sensingUAVvegetation indexproductivitybiomassSilphium perfoliatumРабота выполнена в рамках ГПНИ «Качество и эффективность агропромышленного комплекса»The research was carried out as part of the state scientific and technical program “Quality and Efficiency of Agroindustrial Complex”ReferencesKutsayeva, A. Сreation of management zones for the purposes of land development at the implementation of precision farming in Belarus / А. Kutsayeva, T. Myslyva // Baltic Surveying. – 2020. – Vol. 12. – P. 19–27. https://doi.org/10.22616/j.balticsurveying.2020.003Kutsayeva А., Myslyva T. Сreation of management zones for the purposes of land development at the implementation of precision farming in Belarus. Baltic Surveying, 2020, vol. 12, pp. 19-27. https://doi.org/10.22616/j.balticsurveying.2020.003Daheim, C. Precision agriculture and the future of farming in Europe : scientific foresight study / C. Daheim, K. Poppe, R. Schrijver ; Europ. Parliament. – Brussels : EU, 2016. – 274 p. https://doi.org/10.2861/020809Daheim C., Poppe K., Schrijver R. Precision agriculture and the future of farming in Europe: scientific foresight study. Brussels, European Union, 2016. 274 p. https://doi.org/10.2861/020809Doerge, Т.А. Management zone concepts. Site-specific management guidelines, no. 2 / T.A. Doerge. – Canada : IPNI, 1999. – 4 p.Doerge Т.А. Management zone concepts. Site-specific management guidelines, no. 2. Canada, IPNI, 1999. 4 p.Куцаева О.А. Создание менеджмент-зон для дифференцированного внесения минеральных удобрений с использованием инструментов геостатистики / О.А. Куцаева // Вестн. Белорус. гос. с.-х. акад. – 2020. – №2. – С. 176–181.Kutsaeva O.A. Creation of management zones for differentiated application of mineral fertilizers with the use of geostatistics tools. Vestnik Belorusskoi gosudarstvennoi sel’’skokhozyaistvennoi akademii = Bulletin of the Belarussian State Agricultural Academy, 2020. no. 2, pp. 176-181 (in Russian).Zarco-Tejada, P. J. Precision agriculture: an opportunity for EU farmers – potential support with the cap 2014–2020 / P.J. Zarco-Tejada, N. Hubbard, P. Loudjani ; Europ. Parliament, Directorate-General for Internal Policies of the Union. – Luxembourg : Publ. Office, 2014. – 50 p. https://doi.org/10.2861/58758Zarco-Tejada P. J., Hubbard N., Loudjani P. Precision agriculture: an opportunity for EU farmers - potential support with the cap 2014-2020. Luxembourg, Publications Office, 2014. 50 p. https://doi.org/10.2861/58758Maloku, D. Adoption of precision farming technologies: USA and EU situation / D. Maloku // SEA – Practical Application of Science. – 2020. – Vol. VIII, iss. 22. – P. 7–14.Maloku D. Adoption of precision farming technologies: USA and EU situation. SEA - Practical Application of Science, 2020, vol. VIII, iss. 22, pp. 7-14.Бауэрс, П. Летательные аппараты нетрадиционных схем / П. Бауэрс ; пер. с англ. Б.Б. Рыбака ; под ред. Е.В. Зябрева. – М. : Мир, 2016. – 320 c.Bowers P.M. Unconventional aircraft. 2nd ed. Blue Ridge Summit, TAB Books, 1990. 323 p.Василин, Н.Я. Беспилотные летательные аппараты / Н.Я. Василин. – Минск : Попурри, 2017. – 272 с.Vasilin N. Ya. Unmanned aerial vehicles. Minsk, Popurri Publ., 2017. 272 p. (in Russian).Использование беспилотных летательных аппаратов в сельском хозяйстве / Ю.Н. Зубарев [и др.] // Вестн. Перм. федер. исслед. центра. – 2019. – №2. – С. 47–51. https://doi.org/10.7242/2658-705X/2019.2.5Zubarev Yu. N., Fomin D. S., Chashchin A. N., Zabolotnova M.V. Use of uncleaned aircraft in agriculture. Vestnik Permskogo federal’nogo issledovatel’skogo tsentra = Perm Federal Research Centre Journal, 2019, no. 2, pp. 47-51 (in Russian). https://doi.org/10.7242/2658-705X/2019.2.5Terrestrial laser scanning of agricultural crops / J. Lumme [et al.] // The Intern. Arch. of the Photogrammetry, Remote Sensing a. Spatial Inform. Sciences. – 2008. – Vol. 37, pt. B5. – P. 563–566.Lumme J., Karjalainen M., Kaartinen H., Kukko A., Hyyppä J., Hyyppä H., Jaakkola A., Kleemola J. Terrestrial laser scanning of agricultural crops. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008, vol. 37, pt. B5, pp. 563-566.Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley / J. Bendig [et al.] // Intern. J. of Appl. Earth Observation a. Geoinformation. – 2015. – Vol. 39. – P. 79–87. https://doi.org/10.1016/j.jag.2015.02.012Bendig J., Yu K., Aasen H., Bolten A., Bennertz S., Broscheit J., Gnyp M. L., Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 2015, vol. 39, pp. 79-87. https://doi.org/10.1016/j.jag.2015.02.012Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging / J. Bendig [et al.] // Remote Sensing. – 2014. – Vol. 6, N 11. – P. 10395–10412. https://doi.org/10.3390/rs61110395Bendig J., Bolten A., Bennertz S., Broscheit J., Eichfuss S., Bareth G. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 2014, vol. 6, no. 11, pp. 10395-10412. https://doi.org/10.3390/rs61110395Bendig, J. UAV-based imaging for multi-temporal, very high-resolution crop surface models to monitor crop growth variability / J. Bendig, A. Bolten, G. Bareth // Photogrammetrie. Fernerkundung. Geoinformation. – 2013. – Vol. 6. – Р. 551– 562. https://doi.org/10.1127/1432-8364/2013/0200Bendig J., Bolten A., Bareth G. UAV-based imaging for multi-temporal, very high-resolution crop surface models to monitor crop growth variability. Photogrammetrie. Fernerkundung. Geoinformation, 2013, vol. 6, pp. 551-562. https://doi.org/10.1127/1432-8364/2013/0200Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat / M.A. Hassan [et al.] // Plant Methods. – 2019. – Vol. 15. – Art. 37. https://doi.org/10.1186/s13007-019-0419-7Hassan M. A., Yang M., Fu L., Rasheed A., Zheng B., Xia X., Xiao Y., He Z. Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat. Plant Methods, 2019, vol. 15, art. 37. https://doi.org/10.1186/s13007-019-0419-7Comparison of models in assessing relationship of corn yield with plant height measured during early- to midseason / X. Yin [et al.] // J. of Agr. Science. – 2011. – Vol. 3, N 3. – Р. 14–24. https://doi.org/10.5539/jas.v3n3p14Yin X., Jaja N., McClure M. A., Hayes R.M. Comparison of models in assessing relationship of corn yield with plant height measured during early- to mid-season. Journal of Agricultural Science, 2011, vol. 3, no. 3, pp. 14-24. https://doi.org/10.5539/jas.v3n3p14Mapping and monitoring of biomass and grazing in pasture with an unmanned aerial system / A. Michez [et al.] // Remote Sensing. – 2019. – Vol. 11, N 5. – Art. 473. https://doi.org/10.3390/rs11050473Michez A., Lejeune P., Bauwens S., Herinaina A.A. L., Blaise Y., Munoz E. C., Lebeau F., Bindelle J. Mapping and monitoring of biomass and grazing in pasture with an unmanned aerial system. Remote Sensing, 2019, vol. 11, no. 5, art. 473. https://doi.org/10.3390/rs11050473Evaluation of RGB-based vegetation indices from UAV imagery to estimate forage yield in grassland / U. Lussem [et al.] // The Intern. Arch. of the Photogrammetry, Remote Sensing a. Spatial Inform. Sciences. – 2018. – Vol. XLII-3. – P. 1215–1219. https://doi.org/10.5194/isprs-archives-XLII-3-1215-2018Lussem U., A. Bolten, Gnyp M. L., Jasper J., Bareth G. Evaluation of RGB-based vegetation indices from UAV imagery to estimate forage yield in grassland. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, vol. XLII-3, pp. 1215-1219. https://doi.org/10.5194/isprs-archives-XLII-3-1215-2018RGB vegetation indices applied to grass monitoring: a qualitative analysis / B.D. S. Barbosa [et al.] // Agronomy Research. – 2019. – Vol. 17, N 2. – P. 349–357. https://doi.org/10.15159/AR.19.119Barbosa B.D. S., Ferraz G.A. S., Gonçalves L. M., Marin D. B., Maciel D. T., Ferraz P.F. P., Rossi G. RGB vegetation indices applied to grass monitoring: a qualitative analysis. Agronomy Research, 2019, vol. 17, no. 2, pp. 349-357. https://doi.org/10.15159/AR.19.119Louhaichi, M. Spatially located platform and aerial photography for documentation of grazing impacts on wheat / M. Louhaichi, M.M. Borman, D.E. Johnson // Geocarto Intern. – 2001. – Vol. 16, №1. – P. 65–70. https://doi.org/10.1080/10106040108542184Louhaichi M., Borman M. M., Johnson D.E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 2001, vol. 16, no. 1, pp. 65-70. https://doi.org/10.1080/10106040108542184A visible band index for remote sensing leaf chlorophyll content at the canopy scale / E.R. Hunt Jr. [et al.] // Intern. J. of Appl. Earth Observation a. Geoinformation. – 2013. – Vol. 21. – P. 103–112. doi.org/10.1016/j.jag.2012.07.020Hunt E.R. Jr., Doraiswamy P. C., McMurtrey J. E., Daughtry C.S. T., Perry E. M., Akhmedov B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 2013, vol. 21, pp. 103-112. https://doi.org/10.1016/j.jag.2012.07.020Novel algorithms for remote estimation of vegetation fraction / A.A. Gitelson [et al.] // Remote Sensing of Environment. – 2002. – Vol. 80, N 1. – P. 76–87. https://doi.org/10.1016/s0034-4257(01)00289-9Gitelson A. A., Kaufman Y. J., Stark R., Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002, vol. 80, no. 1, pp. 76-87. https://doi.org/10.1016/s0034-4257(01)00289-9Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation / C.J. Tucker // Remote Sensing of Environment. – 1979. – Vol. 8, N 2. – P. 127–150. https://doi.org/10.1016/0034-4257(79)90013-0Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 1979, vol. 8, no. 2, pp. 127-150. https://doi.org/10.1016/0034-4257(79)90013-0Color indexes for weed identification under various soil, residue, and lighting conditions / D.M. Woebbecke [et al.] // Trans. of the ASAE. – 1995. – Vol. 38, N 1. – Р. 259–269. https://doi.org/10.13031/2013.27838Woebbecke D. M., Meyer G. E., Von Bargen K., Mortensen D.A. Color indexes for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 1995, vol. 38, no. 1, pp. 259-269. https://doi.org/10.13031/2013.27838Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera / A. Beniaich [et al.] // Semina: Ciências Agrárias. – 2019. – Vol. 40, N 1. – Р. 49–66. https://doi.org/10.5433/1679-0359.2019v40n1p49Beniaich A., Silva M.L. N., Avalos F.A. P., Cândido B.M. Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera. Semina: Ciências Agrárias, 2019, vol. 40, no. 1, pp. 49-66. https://doi.org/10.5433/1679-0359.2019v40n1p49Meyer, G.E. Verification of color vegetation indices for automated crop imaging applications / G.E. Meyer, J.C. Neto // Computers a. Electronics in Agriculture. – 2008. – Vol. 63, №2. – P. 282–293. https://doi.org/10.1016/j.compag.2008.03.009Meyer G. E., Neto J.C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 2008, vol. 63, no. 2, pp. 282-293. https://doi.org/10.1016/j.compag.2008.03.009Crop growth estimation system using machine vision / T. Kataoka [et al.] // Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), July 20 – July 24, 2003 / The Inst. of Electrical a. Electronics Engineers. – Kobe, 2003. – Vol. 2. – P. 1079–1083. https://doi.org/10.1109/aim.2003.1225492Kataoka T., Kaneko T., Okamoto H., Hata S. Crop growth estimation system using machine vision. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), July 20 - July 24, 2003. Kobe, 2003, vol. 2, pp. 1079-1083. https://doi.org/10.1109/aim.2003.1225492Marchant, J.A. Shadow invariant classification for scenes illuminated by daylight / J.A. Marchant, C.M. Onyango // J. of the Optical Society of America. – 2000. – Vol. 17, N 11. – P. 1952–1961. https://doi.org/10.1364/josaa.17.001952Marchant J. A., Onyango C.M. Shadow invariant classification for scenes illuminated by daylight. Journal of the Optical Society of America, 2000, vol. 17, no. 11, pp. 1952-1961. https://doi.org/10.1364/josaa.17.001952Hague, Т. Automated crop and weed monitoring in widely spaced cereals / Т. Hague, N.D. Tillett, H. Wheeler // Precision Agriculture. – 2006. – Vol. 7, N 1. – P. 21–32. https://doi.org/10.1007/s11119-005-6787-1Hague Т., Tillett N. D., Wheeler H. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, 2006, vol. 7, no. 1, pp. 21-32. https://doi.org/10.1007/s11119-005-6787-1Automatic segmentation of relevant textures in agricultural images / M. Guijarro [et al.] // Computers a. Electronics in Agriculture. – 2011. – Vol. 75, N 1. – P. 75–83. https://doi.org/10.1016/j.compag.2010.09.013Guijarro M., Рajares G., Riomoros I., Herrera P. J., Burgos-Artizzu X. P., Ribeiro А. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 2011, vol. 75, no. 1, pp. 75-83. https://doi.org/10.1016/j.compag.2010.09.013Estimating the nitrogen status of crops using a digital camera / Y. Li [et al.] // Field Crops Research. – 2010. – Vol. 118, N 3. – P. 221–227. https://doi.org/10.1016/j.fcr.2010.05.011Li Y., Chen D., Walker C. N., Angus J.F. Estimating the nitrogen status of crops using a digital camera. Field Crops Research, 2010, vol. 118, no. 3, pp. 221-227. https://doi.org/10.1016/j.fcr.2010.05.011Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle / Z. Fu [et al.] // Remote Sensing. – 2020. – Vol. 12, N 3. – Art. 508. https://doi.org/10.3390/rs12030508Fu Z., Jiang J., Gao Y., Krienke B., Wang M., Zhong K., Cao Q., Tian Y., Zhu Y., Cao W., Liu X. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing, 2020, vol. 12, no. 3, art. 508. https://doi.org/10.3390/rs12030508

The authors declare that there are no conflicts of interest present.