FORMATION OF A DIGITAL AGRICULTURAL MANAGEMENT SYSTEM BASED ON MONITORING AND LONG-TERM FIELD EXPERIMENTS
DOI 10.12737/2782-490X-2025-58-68 УДК 631.5:004.048
Keywords:
Key words: digital farming, monitoring, artificial intelligence, machine learning, yield forecasting, geographic information systems (GIS), decision support system (DSS)Abstract
The article is devoted to the development of a digital agriculture management system (DAMS) that combines monitoring of agricultural objects, analysis of long-term field experiment data and modern information technologies. The purpose of the study is to create a theoretical basis for optimizing agricultural production management by integrating artificial intelligence, machine learning, remote sensing, geographic information systems (GIS) and big data. The novelty of the work lies in the use of machine learning algorithms for yield forecasting, scaling of agricultural system archetypes based on spatio-temporal analysis, as well as the development of a spatial decision support system (DSS) that combines geographic information systems, modeling and knowledge bases. The technical implementation of monitoring includes the use of unmanned aerial vehicles, weather stations, soil sensors and satellite images, the data of which are processed in cloud environments. The research results demonstrate the possibility of reducing uncertainty in the management of agricultural technologies through predictive analytics, simulation modeling and standardization of multimodal data. The findings emphasize the practical importance of the digital agriculture management system for increasing sustainability, minimizing risks and profitability of crop production. The proposed system allows moving from generalized recommendations to individualized management that takes into account the spatio-temporal variability of agricultural landscapes. The key components of the digital agriculture management system - modules for yield forecasting, analysis of vegetation indices, differentiated fertilization and integration of field experiment data - are focused on solving the problems of digitalization of agriculture. The results of the work are of practical importance for the introduction of intelligent technologies in the agroindustrial complex.
For citation: Kalichkin V.K., Donchenko A.S., Golokhvast K.S. Formation of a digital agriculture management system based on monitoring and long-term field experiments. Agrobiotechnologies and digital farming. 2025; 2(14). 58-68 p.
References
Литература
Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefit/ Maffezzoli F.,
Ardolino M., Bacchetti A., Perona M., Renga F. // Futures. 2022. Vol. 142. P. 102998. https://doi.org/10.1016/
j.futures.2022.102998.
Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview/ Taha M. F., Mao H., Zhang Z. et al. // Agriculture. 2025. Vol. 15. № 6. Р. 582. https://doi.org/10.3390/
agriculture15060582.
Gumbi N., Gumbi L., Twinomurinzi H. Towards sustainable digital agriculture for smallholder farmers: A
systematic literature review // Sustainability. 2023. Vol. 15. № 16. Р. 12530. https://doi.org/10.3390/su151612530.
Akkem Y., Biswas S. K., Varanasi A. Smart farming using artificial intelligence: A review // Engineering
Applications of Artificial Intelligence. 2023. Vol. 120. Р. 105899. https://doi.org/10.1016/j.engappai.2023.105899.
Singh A. K. Precision farming // Water Technology Centre, IARI, New Delhi. 2010. Р. 165-174. URL:
https://drs.icar.gov.in/ebook/EBADAT/6-Other%20Useful%20Techniques/14-Precision%20Farming%
Lecture.pdf. (дата обращения 20.03.2025).
Barrett H., Rose D. C. Perceptions of the fourth agricultural revolution: what’s in, what’s out, and what consequences are anticipated? // Sociologia Ruralis. 2022. Vol. 62. № 2. Р. 162-189. https://doi.org/10.1111/
soru.12324.
Big Data Privacy in Smart Farming: A Review/ Amiri-Zarandi M., Dara R. A, Duncan E., Fraser E. D.
G. // Sustainability. 2022. Vol. 14. № 15. Р. 9120. https://doi.org/10.3390/su14159120.
Ingram J., Maye D. What are the implications of digitalisation for agricultural knowledge? // Frontiers in
Sustainable Food Systems. 2020. Vol. 4. P. 66. https://doi.org/10.3389/fsufs.2020.00066.
An in-depth review on the concept of digital farming/ Ammar E. E., Aziz S. A., Zou X. et al. // Environment,
Development and Sustainability. 2024. Р. 1-27. https://doi.org/10.1007/s10668-024-05161-9.
Kim M-Y., Lee K.H. Electrochemical Sensors for Sustainable Precision Agriculture – A Review // Frontiers
in Chemistry. 2022. Vol. 10. Р. 848320. https://doi.org/10.3389/fchem.2022.848320.
Navarro E., Costa N., Pereira A. A. Systematic Review of IoT Solutions for Smart Farming // Sensors. 2020.
Vol. 20. № 15. Р. 4231. https://doi.org/10.3390/s20154231.
Internet of Things in arable farming: Implementation, applications, challenges and potential/ VillaHenriksen, A., Edwards, G. T., Pesonen, L. A., Green, O., & Sørensen, C. A. G. // Biosystems Engineering.
Vol. 191. pp. 60-84. https://doi.org/10.1016/j.biosystemseng.2019.12.013.
What are the priority research questions for digital agriculture?/ Julie, I., Damian, M., Clive, B., Barnes, A.,
Bear, C., Bell, M., Manning, L. // Land Use Policy. 2022. Vol. 114. P. 105962. https://doi.org/10.1016/
j.landusepol.2021.105962.
Enhancing smart farming through the applications of Agriculture 4.0 technologies/ . Javaid, M., Haleem, A.,
Singh, R. P., & Suman, R. // International Journal of Intelligent Networks. 2022. Vol. 3. pp. 150-164. https://
doi.org/10.1016/j.ijin.2022.09.004.
Kumar S., Khan N. Application of remote sensing and GIS in land resource management // Journal of Geography and Cartography. 2021. Vol. 4. № 2. pp. 78-81. https://doi.org/10.24294//jgc.v4i2.437.
The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture
production systems/ Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. // Current
Opinion in Biotechnology. 2021. Vol. 70. pp. 15-22. https://doi.org/10.1016/j.copbio.2020.09.003/
Geoscience-aware deep learning: A new paradigm for remote sensing/ Ge, Y., Zhang, X., Atkinson, P. M.,
Stein, A., & Li, L. // Science of Remote Sensing. 2022. Vol. 5. Р. 100047. https://doi.org/10.1016/
j.srs.2022.100047.
Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses
and Recent Advances/ Omia, E., Bae, H., Park, E., Kim, M. S., Baek, I., Kabenge, I., & Cho, B. K. // Remote Sensing. 2023. Vol. 15. № 2. Р. 354. https://doi.org/10.3390/rs15020354.
A revisit of internet of things technologies for monitoring and control strategies in smart agriculture /
Rehman A., Saba T., Kashif M. et al. // Agronomy. 2022. Vol. 12. N 1. Р. 127. DOI: 10.3390/agronomy12010127.
Application of smart techniques, internet of things and data mining for resource use efficient and sustainable
crop production/ Ali A., Hussain T., Tantashutikun N. et al // Agriculture. 2023. Vol. 13. N 2. Р. 397. https://
doi.org/10.3390/agriculture13020397.
Каличкин В.К., Максимович К.Ю. Методология формирования цифровой системы управления земледелием // Сибирский вестник сельскохозяйственной науки. 2024. Т. 54. № 3. С. 5-20. https://
doi.org/10.26898/0370- 8799-2024-3-1.
Ullo S. L., Sinha G. R. Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications // Remote Sensing. 2021. Vol. 13. № 13. Р. 2585. https://doi.org/10.3390/rs13132585.
Aaron E.M., Warner T.A., Fang F. Implementation of machine-learning classification in remote sensing: an
applied review // International journal of remote sensing. 2018. Vol. 39. № 9. pp. 2784-2817. https://
doi.org/10.1080/01431161.2018.1433343.
Machine learning in agriculture: A review/ . Liakos K. G., Busato P., Moshou D., Pearson S., & Bochtis,
D. // Sensors. 2018. Vol. 18. № 8. P. 2674. https://doi.org/10.3390/s18082674.
Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends/
Debauche, O., Mahmoudi, S., Manneback, P., & Lebeau, F. // Journal of King Saud University-Computer and Information Sciences. 2022. Vol. 34. № 9. pp. 7494-7514. https://doi.org/10.1016/j.jksuci.2021.09.015.
Machine Learning in Agriculture: A Comprehensive Updated Review/ Benos L., Tagarakis A. C., Dolias G.
et al // Sensors. 2021. Vol. 21. N 11. P. 3758. https://doi.org/10.3390/s21113758.
Каличкин В.К. О необходимости трансформации парадигмы научных исследований в области земледелия (сообщение первое) // Сибирский вестник сельскохозяйственной науки. 2024. Т. 54. № 7. С. 117-129.
https://doi. org/10.26898/0370-8799-2024-7-12.
Каличкин В.К. О необходимости трансформации парадигмы научных исследований в области земледелия (сообщение второе) // Сибирский вестник сельскохозяйственной науки. 2024. Т. 54. № 9. С. 101-114.
https://doi. org/10.26898/0370-8799-2024-9-11.
Milutinović G., Seipel S., Ahonen-Jonnarth U. Geospatial Decision-Making Framework Based on the Concept of Satisficing // ISPRS International Journal of Geo-Information. 2021. Vol. 10. № 5. Р. 326. https://
doi.org/10.3390/ijgi10050326
References
Maffezzoli F, Ardolino M, Bacchetti A, Perona M, Renga F. Agriculture 4.0: a systematic literature review
on the paradigm, technologies and benefit. Futures. 2022. Vol.142. 102998 p. https://doi.org/10.1016/
j.futures.2022.102998.
Taha MF, Mao H, Zhang Z. Emerging technologies for precision crop management towards Agriculture 5.0:
a comprehensive overview. Agriculture. 2025; Vol.15. 6. 582 p. https://doi.org/10.3390/agriculture15060582.
Gumbi N, Gumbi L, Twinomurinzi H. Towards sustainable digital agriculture for smallholder farmers: a
systematic literature review. Sustainability. 2023; Vol.15. 16. 12530 p. https://doi.org/10.3390/su151612530.
Akkem Y, Biswas SK, Varanasi A. Smart farming using artificial intelligence: a review. Engineering Applications of Artificial Intelligence. 2023; Vol.120. 105899 p. https://doi.org/10.1016/j.engappai.2023.105899.
Singh AK. Precision farming. [Internet]. Water Technology Centre, IARI, New Delhi. 2010; 165-174 p.
[cited 2025, March 20]. Available from: https://drs.icar.gov.in/ebook/EBADAT/6-Other%20Useful%
Techniques/14-Precision%20Farming%20Lecture.pdf.
Barrett H, Rose DC. Perceptions of the fourth agricultural revolution: what’s in, what’s out, and what consequences are anticipated? Sociologia Ruralis. 2022; Vol.62. 2. 162-189 p. https://doi.org/10.1111/soru.12324.
Amiri-Zarandi M, Dara RA, Duncan E, Fraser EDG. Big data privacy in smart farming: a review. Sustainability. 2022; Vol.14. 15. 9120 p. https://doi.org/10.3390/su14159120.
Ingram J, Maye D. What are the implications of digitalization for agricultural knowledge? Frontiers in sustainable food systems. 2020; Vol.4. 66 p. https://doi.org/10.3389/fsufs.2020.00066.
Ammar EE, Aziz SA, Zou X. An in-depth review on the concept of digital farming. Environment, Development and Sustainability. 2024; 1-27 p. https://doi.org/10.1007/s10668-024-05161-9.
Kim M-Y, Lee K.H. Electrochemical sensors for sustainable precision agriculture – a review. Frontiers in
Chemistry. 2022; Vol.10. 848320 p. https://doi.org/10.3389/fchem.2022.848320.
Navarro E, Costa N, Pereira AA. Systematic review of iot solutions for smart farming. Sensors. 2020;
Vol.20. 15. 4231 p. https://doi.org/10.3390/s20154231.
Villa-Henriksen A, Edwards GT, Pesonen LA, Green O, Sorensen CAG. Internet of things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering. 2020; Vol.191. 60-84 p.
https://doi.org/10.1016/j.biosystemseng.2019.12.013.
Julie I, Damian M, Clive B, Barnes A, Bear C, Bell M, Manning L. What are the priority research questions
for digital agriculture? Land Use Policy. 2022; Vol.114. 105962 p. https://doi.org/10.1016/
j.landusepol.2021.105962.
Javaid M, Haleem A, Singh RP, Suman R. Enhancing smart farming through the applications of Agriculture
0 technologies. International Journal of Intelligent Networks. 2022; Vol.3. 150-164 p. https://doi.org/10.1016/
j.ijin.2022.09.004.
Kumar S, Khan N. Application of remote sensing and GIS in land resource management. Journal of Geography and Cartography. 2021; Vol.4. 2. 78-81 p. https://doi.org/10.24294//jgc.v4i2.437.
Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. The potential of remote sensing
and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in
Biotechnology. 2021; Vol.70. 15-22 p. https://doi.org/10.1016/j.copbio.2020.09.003/
Ge Y, Zhang X, Atkinson PM, Stein A, Li L. Geoscience-aware deep learning: A new paradigm for remote
sensing. Science of Remote Sensing. 2022; Vol.5. 100047 p. https://doi.org/10.1016/j.srs.2022.100047.
Omia E, Bae H, Park E, Kim MS, Baek I, Kabenge I, Cho BK. Remote sensing in field crop monitoring: a
comprehensive review of sensor systems, data analyses and recent advances. Remote Sensing. 2023; Vol.15. 2. 354
p. https://doi.org/10.3390/rs15020354.
Rehman A, Saba T, Kashif M. A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy. 2022; Vol.12. 1. 127 p. doi: 10.3390/agronomy12010127.
Ali A, Hussain T, Tantashutikun N. Application of smart techniques, internet of things and data mining for
resource use efficient and sustainable crop production. Agriculture. 2023; Vol.13. 2. 397 p. doi: 10.3390/
agriculture13020397.
Kalichkin VK, Maksimovich KYu. [Methodology for the formation of a digital agricultural management
system]. Sibirskiy vestnik selskokhozyaystvennoy nauki. 2024; Vol.54. 3. 5-20 p. https://doi.org/10.26898/0370-
-2024-3-1.
Ullo SL, Sinha GR. Advances in IoT and smart sensors for remote sensing and agriculture applications.
Remote Sensing. 2021; Vol.13. 13. 2585 p. https://doi.org/10.3390/rs13132585.
Aaron EM, Warner TA, Fang F. Implementation of machine-learning classification in remote sensing: an
applied review. International journal of remote sensing. 2018; Vol.39. 9. 2784-2817 p. https://
doi.org/10.1080/01431161.2018.1433343.
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: a review. Sensors.
; Vol.18. 8. 2674 p. https://doi.org/10.3390/s18082674.
Debauche O, Mahmoudi S, Manneback P, Lebeau F. Cloud and distributed architectures for data management in Agriculture 4.0: review and future trends. Journal of King Saud University-Computer and Information Sciences. 2022; Vol.34. 9. 7494-7514 p. https://doi.org/10.1016/j.jksuci.2021.09.015.
Benos L, Tagarakis AC, Dolias G. Machine learning in agriculture: a comprehensive updated review. Sensors. 2021; Vol.21. 11. 3758 p. DOIhttps://doi.org/10.3390/s21113758.
Kalichkin VK. [On the need to transform the paradigm of scientific research in the field of agriculture (first
message)]. Sibirskiy vestnik selskokhozyaystvennoy nauki. 2024; Vol.54. 7. 117-129 p. https://doi. org/10.26898/0370-8799-2024-7-12.
Kalichkin VK. [On the need to transform the paradigm of scientific research in the field of agriculture
(second message)]. Sibirskiy vestnik selskokhozyaystvennoy nauki. 2024; Vol.54. 9. 101-114 p. https://doi. org/10.26898/0370-8799-2024-9-11.
Milutinovic G, Seipel S, Ahonen-Jonnarth U. Geospatial decision-making framework based on the concept
of satisficing. ISPRS International Journal of Geo-Information. 2021; Vol.10. 5. 326 p. https://doi.org/10.3390/
ijgi10050326
