Crop yields prediction using machine learning and remote sensing data in digital agriculture
Keywords:
Data analysis, forecasting, gradient boosting, mathematical analysis, neural networks, random forest, regression analysis, statistics, sustainability, vegetation indicesSynopsis
An analytical review of contemporary scientific literature highlights the high potential of remote sensing data for developing mathematical models to predict agricultural crop productivity at local, regional, and national scales. By integrating multispectral and agrometeorological observations derived from Earth observation platforms, remote sensing enables comprehensive quantification of crop status and environmental dynamics across varying spatial and temporal resolutions. The integration of remote sensing technologies, particularly vegetation and agrometeorological indices derived from Earth observation data, has opened new avenues for yield prediction within the framework of digital agriculture.
As a result of the present study, a suite of machine learning models was developed to assess and predict the productivity of major crops cultivated in Ukraine. These models utilized remote sensing-derived parameters to capture the biophysical conditions of crops throughout the growing season. Vegetation indices, such as NDVI, EVI, NDMI, and VHI, as well as satellite-derived values of PET and LST, were used to build the models for major crop yields. Different machine learning approaches, including regression, ensemble, and decision tree-based algorithms, were applied to the input datasets. A transparent methodological framework was implemented, encompassing data preprocessing, temporal aggregation of indices, model calibration using cross-validation, and independent testing to ensure reproducibility and robustness. The results of statistical evaluation confirmed the reliability, high accuracy, and quality of approximation of most of the developed models, thereby supporting their applicability for both scientific analysis and practical decision-making in the agricultural sector.
In addition to model development, a comprehensive mathematical assessment of the relationship between vegetation indices and crop yields was conducted. This analysis contributed to the theoretical advancement of remote crop monitoring by deepening the understanding of how spectral and meteorological data correlate with plant growth and productivity, particularly when sourced from aerospace imagery. Such relationships were further used to optimize feature selection and enhance model interpretability within a data-driven digital agriculture framework.
At the local scale, the highest predictive performance was observed in regression models for soybean, grain corn, and common bean yields (R² ≥ 0.9, MAPE < 10%). On the regional level, the most accurate models were obtained for sunflower, soybean, rapeseed, and cereals (R² ≈ 0.5–0.7, MAPE < 20%). On the national scale, the best results were recorded for sugar beet, sunflower, and barley (R² ≈ 0.3–0.7). The developed models not only demonstrate strong potential for improving yield forecasting but also represent a valuable contribution to the evolving field of digital and space-based agriculture. Their operational integration into digital agriculture systems can provide real-time decision support for optimizing resource allocation, irrigation scheduling, and risk management. Future research will focus on multisource data fusion, spatiotemporal harmonization, and adaptive learning architectures to improve model transferability across heterogeneous agroecological zones.
References
Fuentes-Peñailillo, F., Gutter, K., Vega, R., Silva, G. C. (2024). Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management. Journal of Sensor and Actuator Networks, 13 (4), 39. https://doi.org/10.3390/jsan13040039
Derkach, O. D., Mykhaylichenko, Y. M. (2021). Digital agriculture: The experience of Ukraine. Mechanization in agriculture & Conserving of the resources, 67 (2), 52–56. Available at: https://stumejournals.com/journals/am/2021/2/52.full.pdf
Chlingaryan, A., Sukkarieh, S., Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69. https://doi.org/10.1016/j.compag.2018.05.012
Idso, S. B., Jackson, R. D., Reginato, R. J. (1977). Remote-Sensing of Crop Yields. Science, 196 (4285), 19–25. https://doi.org/10.1126/science.196.4285.19
de Siqueira, D. A. B., Vaz, C. M. P., da Silva, F. S., Ferreira, E. J., Speranza, E. A., Franchini, J. C. et al. (2024). Estimating Cotton Yield in the Brazilian Cerrado Using Linear Regression Models from MODIS Vegetation Index Time Series. AgriEngineering, 6 (2), 947–961. https://doi.org/10.3390/agriengineering6020054
Servia, H., Pareeth, S., Michailovsky, C. I., de Fraiture, C., Karimi, P. (2022). Operational framework to predict field level crop biomass using remote sensing and data driven models. International Journal of Applied Earth Observation and Geoinformation, 108, 102725. https://doi.org/10.1016/j.jag.2022.102725
Mateo-Sanchis, A., Piles, M., Muñoz-Marí, J., Adsuara, J. E., Pérez-Suay, A., Camps-Valls, G. (2019). Synergistic integration of optical and microwave satellite data for crop yield estimation. Remote Sensing of Environment, 234, 111460. https://doi.org/10.1016/j.rse.2019.111460
Jhajharia, K., Sharma, N. V., Mathur, P. (2025). A Machine Learning Model for Crop Yield Prediction Using Remote Sensing Data. International Research Journal of Multidisciplinary Scope, 6 (2), 577–590. https://doi.org/10.47857/irjms.2025.v06i02.03182
Lykhovyd, P. V. (2018). Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods. Biosystems Diversity, 26 (1), 11–15. https://doi.org/10.15421/011802
Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., Islam, N. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sensing, 14 (9), 1990. https://doi.org/10.3390/rs14091990
Yang, S., Li, L., Fei, S., Yang, M., Tao, Z., Meng, Y. et al. (2024). Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data. Drones, 8 (7), 284. https://doi.org/10.3390/drones8070284
Hara, P., Piekutowska, M., Niedbała, G. (2021). Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data. Land, 10 (6), 609. https://doi.org/10.3390/land10060609
Ziliani, M. G., Altaf, M. U., Aragon, B., Houborg, R., Franz, T. E., Lu, Y. et al. (2022). Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model. Agricultural and Forest Meteorology, 313, 108736. https://doi.org/10.1016/j.agrformet.2021.108736
Rembold, F., Atzberger, C., Savin, I., Rojas, O. (2013). Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing, 5 (4), 1704–1733. https://doi.org/10.3390/rs5041704
Parashar, N., Johri, P., Khan, A. A., Gaur, N., Kadry, S. (2024). An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges. Computers, Materials & Continua, 80 (1), 389–425. https://doi.org/10.32604/cmc.2024.050240
Derkach, O. D., Mykhaylichenko, Y. M. (2021). Digital agriculture: The experience of Ukraine. Mechanization in agriculture & Conserving of the resources, 67 (2), 52–56. Available at: https://stumejournals.com/journals/am/2021/2/52.full.pdf
Chlingaryan, A., Sukkarieh, S., Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69. https://doi.org/10.1016/j.compag.2018.05.012
Idso, S. B., Jackson, R. D., Reginato, R. J. (1977). Remote-Sensing of Crop Yields. Science, 196 (4285), 19–25. https://doi.org/10.1126/science.196.4285.19
de Siqueira, D. A. B., Vaz, C. M. P., da Silva, F. S., Ferreira, E. J., Speranza, E. A., Franchini, J. C. et al. (2024). Estimating Cotton Yield in the Brazilian Cerrado Using Linear Regression Models from MODIS Vegetation Index Time Series. AgriEngineering, 6 (2), 947–961. https://doi.org/10.3390/agriengineering6020054
Servia, H., Pareeth, S., Michailovsky, C. I., de Fraiture, C., Karimi, P. (2022). Operational framework to predict field level crop biomass using remote sensing and data driven models. International Journal of Applied Earth Observation and Geoinformation, 108, 102725. https://doi.org/10.1016/j.jag.2022.102725
Mateo-Sanchis, A., Piles, M., Muñoz-Marí, J., Adsuara, J. E., Pérez-Suay, A., Camps-Valls, G. (2019). Synergistic integration of optical and microwave satellite data for crop yield estimation. Remote Sensing of Environment, 234, 111460. https://doi.org/10.1016/j.rse.2019.111460
Jhajharia, K., Sharma, N. V., Mathur, P. (2025). A Machine Learning Model for Crop Yield Prediction Using Remote Sensing Data. International Research Journal of Multidisciplinary Scope, 6 (2), 577–590. https://doi.org/10.47857/irjms.2025.v06i02.03182
Lykhovyd, P. V. (2018). Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods. Biosystems Diversity, 26 (1), 11–15. https://doi.org/10.15421/011802
Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., Islam, N. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sensing, 14 (9), 1990. https://doi.org/10.3390/rs14091990
Yang, S., Li, L., Fei, S., Yang, M., Tao, Z., Meng, Y. et al. (2024). Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data. Drones, 8 (7), 284. https://doi.org/10.3390/drones8070284
Hara, P., Piekutowska, M., Niedbała, G. (2021). Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data. Land, 10 (6), 609. https://doi.org/10.3390/land10060609
Ziliani, M. G., Altaf, M. U., Aragon, B., Houborg, R., Franz, T. E., Lu, Y. et al. (2022). Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model. Agricultural and Forest Meteorology, 313, 108736. https://doi.org/10.1016/j.agrformet.2021.108736
Rembold, F., Atzberger, C., Savin, I., Rojas, O. (2013). Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing, 5 (4), 1704–1733. https://doi.org/10.3390/rs5041704
Parashar, N., Johri, P., Khan, A. A., Gaur, N., Kadry, S. (2024). An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges. Computers, Materials & Continua, 80 (1), 389–425. https://doi.org/10.32604/cmc.2024.050240
Text Identifier of this Chapter
Chapter 1
Published
December 4, 2025
Copyright (c) 2026 Pavlo Lykhovyd
How to Cite
Lykhovyd, P. (2025). Crop yields prediction using machine learning and remote sensing data in digital agriculture. In P. Lykhovyd, V. Silchenko, L. Kononenko, V. Savchenko, A. Karnaushenko, Y. Hlavatska, H. Savelenko, N. Sysolina, I. Sysolina, K. Vasylkovska, O. Andriienko, V. Malakhovska, I. Androshchuk, O. Vasylkovskyi, S. Moroz, G. Karashchuk, & E. Kulidzhanov, In Press. Innovative technological solutions in agriculture. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/innovative_technological_solutions_in_agriculture/chapter/160
