Integration of RS data and UAV data for agricultural productivity assessment and differential fertilization in Kazakhstan

DOI: 10.35595/2414-9179-2025-2-31-425-441

View or download the article (Eng)

About the Authors

Baitak Apshikur

D. Serikbayev East Kazakhstan Technical University, School of Earth Sciences,
69, Protozanova str., Ust-Kamenogorsk, 070004, Kazakhstan,
E-mail: bapshikur@edu.ektu.kz

Elibek A. Asangaliyev

D. Serikbayev East Kazakhstan Technical University, School of Earth Sciences,
69, Protozanova str., Ust-Kamenogorsk, 070004, Kazakhstan,
E-mail: elibek60@mail.ru

Murat A. Rakhimov

Abylkas Saginov Karaganda Technical University, Department of Building Materials and Technology,
56, Nursultana Nazarbayeva ave., Karaganda, 100027, Kazakhstan,
E-mail: rahimov67@mail.ru

Elena V. Medvedeva

D. Serikbayev East Kazakhstan Technical University, School of Earth Sciences,
69, Protozanova str., Ust-Kamenogorsk, 070004, Kazakhstan,
E-mail: emedvedeva@edu.ektu.kz

Abstract

This study explores the use of Unmanned Aerial Vehicles “UAVs” and Remote Sensing “RS” data to assess the productivity of agricultural lands in various regions of Kazakhstan. The primary focus is on analyzing vegetation indicators such as the Normalized Difference Vegetation Index “NDVI” and their relationship with the agrochemical properties of the soil. A comparative analysis was performed between satellite multispectral imaging and UAV-based aerial photography to determine the most accurate methods for predicting spring barley yield. The integration of RS technologies with traditional agricultural methods has proven to reduce environmental impact, minimize soil degradation, and enhance the stability of agroecosystems. Field experiments took place in three different soil-climatic zones of Kazakhstan, with spring barley cultivated under controlled conditions. The study included multispectral imaging from Landsat-8–9 satellites and UAV-based multispectral imaging. The results indicate that differentiated fertilization strategies, based on the spatial distribution of soil nutrients, contribute to increased crop productivity and reduced excessive nitrogen application. The findings of this study highlight that the implementation of precision agricultural technologies can significantly improve yield prediction accuracy and resource efficiency. Moreover, integrating UAV-based monitoring with GIS mapping enables real-time decision-making in farm management. These approaches promote sustainable agricultural practices by reducing soil degradation and enhancing long-term productivity through environmentally friendly farming methods. The results of this study are applicable to the development of sustainable agricultural strategies aimed at enhancing productivity while conserving natural resources, thereby ensuring long-term food security and environmental stability.

Keywords

yield prediction, satellite multispectral image, field scale, barley, vegetation index

References

  1. Abedzhanova A.S., Dzhaxylykov A.F., Rasskazov P.A., Apshikur B., Islyam G. Automated System for Optimizing the Movement of Urban Passenger Transport Using GIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023. V. XLVIII-5/W2-2023. P. 1–8. DOI: 10.5194/ISPRS-Archives-XLVIII-5-W2-2023-1-2023.
  2. Apshikur B., Alimkulov M.A., Kapasov A.K., Toleubekyzy I.T. Innovative Technology for Assessing the Degradation of the Earth by Sand Desertification of Soils with Specialized Processing of Space Materials. InterCarto. InterGIS. Moscow: Lomonosov Moscow State University, Faculty of Geography, 2024. V. 30. Part 2. P. 223–235 (in Russian). DOI: 10.35595/2414-9179-2024-2-30-223-235.
  3. Apshikur B., Kurmangaliyev T.B., Goltsev A.G., Alimkulov M.M., Kapasov A.K. The Method of Multi-Criteria Analysis for Determining the Flood-Hazardous Area and the Development of Protective Structures. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023. V. XLVIII-5/W2-2023. P. 9–17. DOI: 10.5194/ISPRS-archives-XLVIII-5-W2-2023-9-2023.
  4. Apshikur B., Maksimov V.A., Toleubekyzy I.T., Alimkulov M.M. Current Assessment of Atmospheric Pollution by Industrial Enterprises in Ust-Kamenogorsk Based on GIS Technologies. InterCarto. InterGIS. Moscow: Lomonosov Moscow State University, Faculty of Geography, 2024. V. 30. Part 2. P. 469–481 (in Russian). DOI: 10.35595/2414-9179-2024-2-30-469-481.
  5. Apshikur B., Rakhymberdina M., Levin E., Toguzova M., Daumova G., Assylkhanova Zh., Kapasov A., Kolpakova V. Integrated Research of the Transboundary Yertis River Using Modern Monitoring Methods and Bathymetric Survey. ES Energy & Environment, 2025. V. 27. P. 1362. DOI: 10.30919/esee1362.
  6. Asangaliyev E.A., Apshikur B., Lutay S.S., Assylkhanova Z.A. Spatial Analysis and Mapping of Potential Wildfires from Landsat Satellite Data. InterCarto. InterGIS. Moscow: Lomonosov Moscow State University, Faculty of Geography, 2024. V. 30. Part 1. P. 476–490 (in Russian). DOI: 10.35595/2414-9179-2024-1-30-476-490.
  7. Bakaeva N.P., Vasiliev A.S., Zakharova O.A. Efficiency of Herbicide Treatment Against Weed Vegetation in Intensive Technology of Spring Barley Cultivation. Bulletin of Samara State Agricultural Academy, 2024. V. 9. No. 1. P. 18–26. DOI: 10.55170/1997-3225-2024-9-1-18-26.
  8. Beluhova-Uzunova R.P., Dunchev D.M. Precision Farming—Concepts and Perspectives. Problems of Agricultural Economics, 2019. V. 360. No. 3. P. 142–155. DOI: 10.30858/zer/112132.
  9. Boiarskii B., Hasegawa H. Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content. Journal of Mechanics of Continua and Mathematical Sciences, 2019. Special Iss. No. 4. P. 20–29. DOI: 10.26782/jmcms.spl.4/2019.11.00003.
  10. Cao Zh., Kooistra L., Wang W., Guo L., Valente J. Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review. Drones, 2023. V. 7. No. 10. P. 620–629. DOI: 10.3390/drones7100620.
  11. Deikun V., Zhuk D., Machok Y. Overview of Application Methods and Application Efficiency Mineral Fertilizers. National Interagency Scientific and Technical Collection of Works. Design, Production and Exploitation of Agricultural Machines, 2022. P. 41–47. DOI: 10.32515/2414-3820.2022.52.41-47.
  12. Erunova M., Simakina A., Yakubailik O. Smart Analysis of Agricultural Land Use with NDVI at Kuraginskoye Agricultural Experimental Production Facility. IOP Conference Series: Earth and Environmental Science, 2021. V. 677. P. 032105. DOI: 10.1088/1755-1315/677/3/032105.
  13. Fan D. Research on the Establishment of NDVI Long-Term Data Set Based on a Novel Method. Scientific Reports, 2023. V. 13. No. 1. P. 337–347. DOI: 10.1038/s41598-023-36939-y.
  14. Ganie M.A., Nusrath A. Determining the Vegetation Indices (NDVI) from Landsat-8 Satellite Data. International Journal of Advanced Research, 2016. V. 4. No. 8. P. 1459–1463. DOI: 10.21474/IJAR01/1348.
  15. Gonçalves J., Ferraz G., Reynaldo É., Marin D., Ferra P., Pérez-Ruiz M., Rossi G., Vieri M., Sarri D. Comparative Analysis of Soil-Sampling Methods Used in Precision Agriculture. Journal of Agricultural Engineering, 2021. V. 52. No. 1. P. 1–12. DOI: 10.4081/jae.2021.1117.
  16. Górski R., Robert R., Niewiadomska A., Wolna-Maruwka A., Plaza A. Innovative Spring Barley Cultivation Technology Based on the Use of Microbial Products Together with Living Mulch in Organic Farming. Agronomy, 2023. V. 13. Iss. 7. P. 1914. DOI: 10.3390/agronomy13071914.
  17. Grados D., Schrevens E. Cassava NDVI Analysis: A Nonlinear Mixed Model Approach Based on UAV-Imagery. Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2020. V. 88. No. 3. P. 337–347. DOI: 10.1007/s41064-020-00116-x.
  18. Gurov A., Shirokova V., Khutorova A., Yurova Yu. Features of Remote Determination of Humus Content in Arable Soils. Advances in Engineering Research VIII All-Russian Science and Technology Conference, 2019. V. 182. No. 3. P. 138–144. DOI: 10.2991/ciggg-18.2019.25.
  19. Hoffland E., Kuyper Th., Coman R., Creamer R. Eco-Functionality of Organic Matter in Soils. Plant Soil, 2020. V. 455. P. 1–22. DOI: 10.1007/s11104-020-04651-9.
  20. Huang Y., Reddy K.N., Fletcher R.S., Pennington D. UAV Low-Altitude Remote Sensing for Precision Weed Management. Weed Technology, 2018. V. 32. No. 1. P. 2–6. DOI: 10.1017/wet.2017.89.
  21. Huang Sh., Tang L., Hupy J., Wang Y., Shao G. A Commentary Review on the Use of Normalized Difference Vegetation Index (NDVI) in the Era of Popular Remote Sensing. Journal of Forestry Research, 2021. V. 32. P. 1–6. DOI: 10.1007/s11676-020-01155-1.
  22. Huuskonen J., Oksanen T. Soil Sampling with Drones and Augmented Reality in Precision Agriculture. Computers and Electronics in Agriculture, 2018. V. 154. P. 25–35. DOI: 10.1016/j.compag.2018.08.039.
  23. Jindo K., Kozan O., Iseki K., Maestrini B., Van Evert F., Yilma W., Arai E., Shimabukuro Y., Sawada Y., Kempenaar C. Potential Utilization of Satellite Remote Sensing for Field-Based Agricultural Studies. Chemical and Biological Technologies in Agriculture, 2021. V. 8. No. 58. DOI: 10.1186/s40538-021-00253-4.
  24. Korchagin A., Mazirov M., Shchukin I., Shchukina W., Petrosyan R., Shilov P. Differentiated Fertilization in the System of Precision Farming on the Complex of Gray Forest Soils in Vladimir Opolye. IOP Conference Series: Earth and Environmental Science, 2021. V. 852. P. 012051. DOI: 10.1088/1755-1315/852/1/012051.
  25. Kulyasov N., Novik N., Klyukin N., Charyyarova G. Precision Agriculture in the Russian Federation: Problems and Directions in Development. IOP Conference, Series: Earth and Environmental Science, 2020. V. 548. P. 022090. DOI: 10.1088/1755-1315/548/2/022090.
  26. Kurbanov R.K., Zakharova N.I. Application of Vegetation Indexes to Assess the Condition of Crops. Agricultural Machinery and Technologies, 2020. V. 14. No. 4. P. 4–11 (in Russian). DOI: 10.22314/2073-7599-2020-14-4-4-11.
  27. Lebrini Y., Boudhar A., Htitiou A., Hadria R., Lionboui H., Bounoua L., Benabdelouahab T. Remote Monitoring of Agricultural Systems Using NDVI Time Series and Machine Learning Methods: A Tool for an Adaptive Agricultural Policy. Arabian Journal of Geosciences, 2020. V. 13. P. 796. DOI: 10.1007/s12517-020-05789-7.
  28. Lukin S.V. Dynamics of the Agrochemical Fertility Parameters of Arable Soils in the Southwestern Region of Central Chernozemic Zone of Russia. Eurasian Soil Science, 2017. V. 50. P. 1323–1331. DOI: 10.1134/S1064229317110096.
  29. Michaud A., Cambier Ph., Sappin-Didier V., Deltreil V., Mercier V., Rampon J.-N., Houot S. Mass Balance and Long-Term Soil Accumulation of Trace Elements in Arable Crop Systems Amended with Urban Composts or Cattle Manure During 17 Years. Environmental Science and Pollution Research, 2020. V. 27. P. 5367–5386. DOI: 10.1007/s11356-019-07166-8.
  30. Muraru S., Muraru V., Condruz P., Ionel C., Sfiru R. Considerations on the Importance of the Main Chemical Properties of the Soil in Agrochemical Studies. E3S Web of Conferences, 2021. V. 286. P. 03019. P. 1–9. DOI: 10.1051/e3sconf/202128603019.
  31. Pan W., Wang X., Sun Y., Wang J., Li Y., Li Sh. Karst Vegetation Coverage Detection Using UAV Multispectral Vegetation Indices and Machine Learning Algorithm. Plant Methods, 2023. V. 19. No. 7. P. 1–16. DOI: 10.1186/s13007-023-00982-7.
  32. Petukhov D., Ivanov A., Bondarenko E., Trubnikov A., Semizorov S. The Efficiency of the Differentiated Application of Mineral Fertilizers in the Production Technology of Winter Wheat Cultivation. IOP Conference Series, Earth and Environmental Science, 2021. V. 723. P. 032042. P. 1–5. DOI: 10.1088/1755-1315/723/3/032042.
  33. Phang S.K., Chiang T.H.A., Happonen A., Chang M.M.L. From Satellite to UAV-Based Remote Sensing: A Review on Precision Agriculture. IEEE Access, 2023. V. 11. P. 127057–127076. DOI: 10.1109/ACCESS.2023.3330886.
  34. Ryzhkov S.O., Portnov V.S., Huangan N.Kh., Rakhimov M., Khmyrova E.N. Research into Stability of Tailings Storage at Vostochnaya Coal Processing Plant (Central Kazakhstan) to Assess its Safe Conservation and Abandonment. Ugol, 2021. No. 12-2021/1149/. P. 57–62 (in Russian). DOI: 10.18796/0041-5790-2021-12-57-62.
  35. Sadenova M.A., Beisekenov N.A., Apshikur B., Khrapov S.S., Kapasov A.K., Mamysheva A.M., Klemeš J.J. Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods. Chemical Engineering Transactions, 2022. V. 94. P. 697–702. DOI: 10.3303/CET2294116.
  36. Shiwakoti S., Zheljazkov V.D., Gollany H.T., Kleber M., Xing B., Astatkie T. Macronutrient in Soils and Wheat from Long-Term Agroexperiments Reflects Variations in Residue and Fertilizer Inputs. Scientific Reports, 2020. V. 10. P. 3263. DOI: 10.1038/s41598-020-60164-6.
  37. Surinov A.V. Agro-Ecological Assessment of the Condition of Arable Soils of the CCR. IOP, Conference Series, 2023. V. 1206. P. 012011. P. 1–8. DOI: 10.1088/1755-1315/1206/1/012011.
  38. Toguzova M.M., Rakhymberdina M.Y., Kulenova N.A., Shaimardanov Z.K., Assylkhanova Zh.A., Apshikur B., Beisekenov N.A. Analysis of Process Modeling in Modern Software Program to Support “Smart” Agriculture. Chemical Engineering Transactions, 2022. V. 94. P. 871–876. DOI: 10.3303/CET2294145.
  39. Tucher Von S., Hörndl D., Schmidhalter U. Interaction of Soil pH and Phosphorus Efficacy: Long-Term Effects of P Fertilizer and Lime Applications on Wheat, Barley, and Sugar Beet. Ambio, 2018. V. 47 (Suppl. 1). P. 41–49. DOI: 10.1007/s13280-017-0970-2.
  40. Vlasenko N.G., Korotkikh N.A., Myakisheva O.A. Response of Spring Barley Subspecies to Herbicides. Russian Agricultural Sciences, 2011. V. 37. P. 465–468 (in Russian) DOI: 10.3103/S1068367411060206.
  41. Yang Z., Bai Z., Qin Z. A New Soil Sampling Design Method Using Multi-Temporal and Spatial Data Fusion. Environmental Science and Pollution Research International, 2022. V. 29. P. 21023–21033. DOI: 10.1007/s11356-021-17200-3.
  42. Yapiyev V., Gilman Ch., Kabdullayeva T., Suleimenova A., Shagadatova A., Duisembay A., Naizabekov S., Mussurova S., Sydykova K., Raimkulov I., Kabimoldayev I., Abdrakhmanova A., Omarkulova S., Nurmukhambetov D., Kudarova A., Malgazhdar D., Schönbach Ch., Inglezakis V. Top Soil Physical and Chemical Properties in Kazakhstan Across a North-South Gradient. Scientific Data, 2018. V. 5. P. 180242. DOI: 10.1038/sdata.2018.242.
  43. Zolkin A., Shavanov M., Hmizova N. Features of the Use of Information Technologies in Agriculture. IOP Conference Series: Earth and Environmental Science, 2021. V. 677. P. 032091. P. 1–5. DOI: 10.1088/1755-1315/677/3/032091.

For citation: Apshikur B., Asangaliyev E.A., Rakhimov M.A., Medvedeva E.V. Integration of RS data and UAV data for agricultural productivity assessment and differential fertilization in Kazakhstan. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 2. P. 425–441. DOI: 10.35595/2414-9179-2025-2-31-425-441