In Press. Innovative technological solutions in agriculture
Keywords:
Data analysis, forecasting, gradient boosting, mathematical analysis, neural networks, random forest, regression analysis, statistics, sustainability, vegetation indicesSynopsis
The book "Innovative technological solutions in agriculture" offers a clear and accessible overview of modern digital tools and technologies that are changing the way agriculture works. It explains how satellite images, weather data, and machine learning help farmers better monitor crops, predict yields, and use resources more efficiently. Using examples such as advanced yield prediction models for major crops in Ukraine, the book shows how these technologies work in practice. It also brings together research on sustainable farming, climate-smart innovations, robotics, and digital farm management systems. Overall, the book highlights how new technologies can increase productivity, reduce environmental impact, and support the development of smarter and more resilient agricultural systems.
Chapters
Downloads
Download data is not yet available.
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
Downloads
Published
December 4, 2025
Copyright (c) 2025 Pavlo Lykhovyd, Volodymyr Silchenko, Lesia Kononenko, Vira Savchenko, Alla Karnaushenko, Yuliia Hlavatska, Hryhorii Savelenko, Nataliia Sysolina, Iryna Sysolina, Kateryna Vasylkovska, Olha Andriienko, Valentyna Malakhovska, Ilona Androshchuk, Oleksii Vasylkovskyi, Serhii Moroz
How to Cite
Lykhovyd, P., Silchenko, V., Kononenko, L., Savchenko, V., Karnaushenko, A., Hlavatska, Y., Savelenko, H., Sysolina, N., Sysolina, I., Vasylkovska, K., Andriienko, O., Malakhovska, V., Androshchuk, I., Vasylkovskyi, O., Moroz, S., Karashchuk, G., & Kulidzhanov, E. (2025). In Press. Innovative technological solutions in agriculture. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/innovative_technological_solutions_in_agriculture
