Digital Twin-Driven Precision Irrigation and Crop Health Monitoring Using Multisource Remote Sensing Data
Keywords:
digital twin; precision agriculture; remote sensing; irrigation management; crop health; multisource data fusionAbstract
The escalating pressures of climate variability, population growth, and water scarcity demand a fundamental transformation in agricultural water management and crop protection. This paper presents a comprehensive systems-level analysis of digital twin-driven precision irrigation and crop health monitoring, integrating multisource remote sensing data within a unified cyber-physical framework. Moving beyond isolated sensor networks and static decision models, we articulate an architecture in which a living digital replica of the farm continuously synchronizes satellite, unmanned aerial vehicle, and ground-based sensor streams to capture spatiotemporal heterogeneity in soil moisture, plant water status, and disease emergence. The discussion emphasizes structural trade-offs involving latency, data fusion complexity, model drift, and the federation of edge and cloud intelligence. We examine how the digital twin construct reframes irrigation from a prescheduled operation to a model-predictive control process that balances yield objectives with resource constraints. Robustness against sensor degradation, domain adaptation across crop varieties, and the fairness implications of technology access for smallholder farmers are addressed as integral governance challenges. The paper further explores the deployment infrastructure, including communication backbones, energy autonomy, and interoperability standards necessary to scale such systems across diverse agroecological zones. By coupling high-resolution crop health diagnostics with closed-loop irrigation actuation, the digital twin paradigm can enhance water productivity, reduce chemical inputs, and contribute to climate-resilient food systems. Through detailed conceptual analysis and cross-domain comparisons, this work delineates the architectural principles, policy prerequisites, and long-term sustainability pathways for operationalizing digital twins in agriculture.
References
1. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415.
2. Pylianidis, C., Osinga, S., & Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, 105942.
3. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
4. Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: a review on crop data management. Agronomy, 10(2), 207.
5. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.
6. Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402.
7. Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2021). Estimates of plant density of wheat crops from UAV-based multispectral images. Precision Agriculture, 22, 1193-1214.
8. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., ... & Goudos, S. K. (2022). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things, 18, 100187.
9. 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.
10. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
11. Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349.
12. Antle, J. M., Basso, B., Conant, R. T., Godfray, H. C. J., Jones, J. W., Herrero, M., ... & Wheeler, T. R. (2017). Towards a new generation of agricultural system data, models and knowledge products: design and improvement. Agricultural Systems, 155, 255-268.
13. Skobelev, P. O., Simonova, E. V., Smirnov, S. V., Budaev, D. S., Voshchuk, G. Y., & Morokov, A. L. (2020). Development of a digital twin of a plant for precision agriculture. In 2020 IV International Conference on Information Technologies in Engineering Education (Inforino) (pp. 1-6). IEEE.
14. Van der Velde, M., Van Diepen, C. A., Baruth, B., Eerens, H., Orlandi, S., Niemeyer, S., ... & Boogaard, H. L. (2019). The JRC MARS crop yield forecasting system: activities, advances, and perspectives. Agricultural Systems, 168, 231-241.
15. Li, W., Dong, R., Fu, H., & Yu, L. (2019). Large-scale oil palm tree detection from high-resolution satellite images using deep learning. Remote Sensing, 11(1), 11.
16. Eichi, V. R., Anderer, C., Mistele, B., & Schmidhalter, U. (2020). UAV-based thermal and RGB imaging for water stress detection in wheat. Precision Agriculture, 21, 387-406.
17. Abdalla, A., Wheeler, T. A., Dever, J., Lin, Z., Arce, J., & Guo, W. (2024). Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model. Biosystems Engineering, 237, 220-231.
18. Jiménez-Brenes, F. M., López-Granados, F., Torres-Sánchez, J., Peña, J. M., & de Castro, A. I. (2019). Quantifying pruning impacts on olive canopy architecture and yield using UAV imagery and OBIA. Precision Agriculture, 20, 723-744.
19. Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., ... & Torre Neto, A. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276.
20. Perea, R. G., Poyato, E. C., Montesinos, P., & Díaz, J. A. R. (2019). Prediction of irrigation event occurrence using IoT and machine learning models. Water Resources Management, 33, 1593-1607.
21. Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency: A systematic review. Smart Agricultural Technology, 2, 100036.
22. Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4), 395-420.
23. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
24. Khatri, K. L., Kumar, V., Sharma, K., & Singh, P. (2020). IoT based smart irrigation system: a comprehensive review. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 543-549). IEEE.
25. Ahmad, L., & Nabi, F. (2021). Agriculture 5.0: artificial intelligence, IoT and machine learning. CRC Press.
26. Li, G., Yuan, H., Chen, S., Hu, Q., Wang, J., & Jiang, K. (2026). MFT: Memory-Aware Fine-Tuning of SAM2 for Efficient Long-Sequence Video Object Segmentation. IEEE Signal Processing Letters.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
