An Energy-Efficient Hybrid System Combining Sentinel-2 Satellite Data and Ground-Based Single-Pixel Detector for Crop Monitoring
Source abstract
Abstract
Precision agriculture will continue to heavily rely on data-driven models to enable more intensive crop monitoring and data-driven decisions. The available remote sensing techniques, particularly those based on multispectral Sentinel-2 data, still have major shortcomings due to cloud cover, low temporal resolution, and time lags in data availability. To address these shortcomings, this paper proposes a hybrid approach that combines Sentinel-2 satellite data with real-time data generated by low-cost ground-based single-pixel detectors (SPDs), such as the AS7263. This hybrid approach addresses key shortcomings in existing agricultural monitoring systems and offers a cost-effective, scalable solution for real-time monitoring and prediction of end-of-season yield, moisture, and plant height using simple PLRS models implemented directly in SPDs with an energy-efficient algorithm for deployment on the STM32G030 microcontroller.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing in Agriculture · Smart Agriculture and AI · Soil Moisture and Remote Sensing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J. J. Strossmayer University of Osijek, 31000 Osijek, Croatia
Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Agricultural Institute Osijek, 31000 Osijek, Croatia
References (38)
Show source-provided references
1. Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems10.1080/10496505.2019.1638264 ↗
2. The digitization of agricultural industry—A systematic literature review on agriculture 4.010.1016/j.atech.2022.100042 ↗
3. The Interplay between the Internet of Things and agriculture: A bibliometric analysis and research agenda10.1016/j.iot.2022.100580 ↗
4. Advancements in Agriculture 4.0 and the Needs for Introduction and Adoption in Ethiopia: A Review
5. Significant remote sensing vegetation indices: A review of developments and applications10.1155/2017/1353691 ↗
6. Analysis of normalized difference vegetation index (NDVI) multi-temporal series for the production of forest cartography
7. Segarra, J., Buchaillot, M.L., Araus, J.L., and Kefauver, S.C. (2020). Remote Sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy, 10.10.3390/agronomy10050641 ↗
8. Spectral bands vs. vegetation indices: An AutoML approach for processing tomato yield predictions based on Sentinel-2 imagery10.1016/j.atech.2025.100805 ↗
9. Handheld arduino-based near infrared spectrometer for non-destructive quality evaluation of siamese oranges10.1088/1755-1315/653/1/012119 ↗
10. A Miniature Sensor for Measuring Reflectance, Relative Humidity, and Temperature: A Greenhouse Example
11. A new, low-cost ground-based NDVI sensor for manual and automated crop monitoring10.1016/j.atech.2025.100892 ↗
12. Lopin, P., Nawsang, P., Laywisadkul, S., and Lopin, K.V. (2025). Evaluation of Low-Cost Multi-Spectral Sensors for Measuring Chlorophyll Levels Across Diverse Leaf Types. Sensors, 25.10.20944/preprints202503.1183.v1 ↗
13. Bollas, N., Kokinou, E., and Polychronos, V. (2021). Comparison of sentinel-2 and UAV multispectral data for use in precision agriculture: An application from Northern Greece. Drones, 5.10.3390/drones5020035 ↗
14. Magno, R., Rocchi, L., Dainelli, R., Matese, A., Di Gennaro, S.F., Chen, C.-F., Son, N.-T., and Toscano, P. (2021). Agroshadow: A new sentinel-2 cloud shadow detection tool for precision agriculture. Remote Sens., 13.10.3390/rs13061219 ↗
15. Bukowiecki, J., Rose, T., and Kage, H. (2021). Sentinel-2 data for precision agriculture?—A UAV-based assessment. Sensors, 21.10.3390/s21082861 ↗
16. Assessments on the impact of high-resolution-sensor pixel sizes for Common Agricultural Policy and Smart Farming Services in European regions10.1016/j.compag.2019.105205 ↗
17. Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation10.1109/JSTARS.2015.2400134 ↗
18. Martínez-Casasnovas, J.A., Uribeetxebarría, A., Escolà, A., and Arnó, J. (2019). Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield. Precision Agriculture ’19, Wageningen Academic Publishers.10.3920/978-90-8686-888-9_38 ↗
19. Crop classification from Sentinel-2-derived vegetation indices using ensemble learning10.1117/1.JRS.12.026019 ↗
20. Ma, T., Hu, Y., Wang, J., Beckline, M., Pang, D., Chen, L., Ni, X., and Li, X. (2023). A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China. Remote Sens., 15.10.3390/rs15071853 ↗
21. Application of NDVI in Vegetation Monitoring using Sentinel-2 Data for Shrirampur Region of Maharashtra10.20546/ijcmas.2021.1001.098 ↗
22. Alvarez-Mozos, J., Villanueva, J., Arias, M., and Gonzalez-Audicana, M. (2021, January 11–16). Correlation between NDVI and Sentinel-1 Derived Features for Maize. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.10.1109/IGARSS47720.2021.9554099 ↗
23. Crop growth monitoring through Sentinel and Landsat data based NDVI time-series10.18287/2412-6179-CO-635 ↗
24. Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index10.1016/j.cj.2019.06.005 ↗
25. Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity10.1109/TGRS.2012.2235447 ↗
26. Antognelli, S. (2024, April 22). The Great Value of Vegetation Indices from Satellite for Farmers. Available online: https://www.copernicus.eu/system/files/2020-12/NT%2025nov%20-%20The%20great%20value%20of%20vegetation%20indices%20from%20satellite%20for%20farmers.pdf.
27. Evaluation and Correlation of Sentinel-2 NDVI and NDMI in Kyiv (2017–2021)10.12911/22998993/151884 ↗
28. Ren, Y., Zhou, J., Chen, J., Tao, S., Qin, L., Li, G., Lin, Z., and Luo, H. (2020). Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data. Remote Sens., 12.10.3390/rs12111712 ↗
29. Harmse, C.J., Gerber, H., and van Niekerk, A. (2022). Evaluating Several Vegetation Indices Derived from Sentinel-2 Imagery for Quantifying Localized Overgrazing in a Semi-Arid Region of South Africa. Remote Sens., 14.10.3390/rs14071720 ↗
30. Fang, G., He, X., Weng, Y., and Fang, L. (2023). Texture Features Derived from Sentinel-2 Vegetation Indices for Estimating and Mapping Forest Growing Stock Volume. Remote Sens., 15.10.3390/rs15112821 ↗
31. Bellvert, J., Pelechá, A., Pamies-Sans, M., Virgili, J., Torres, M., and Casadesús, J. (2023). Assimilation of Sentinel-2 Biophysical Variables into a Digital Twin for the Automated Irrigation Scheduling of a Vineyard. Water, 15.10.3390/w15142506 ↗
32. Spišić, J., Šimić, D., Balen, J., Jambrović, A., and Galić, V. (2022). Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit. Remote Sens., 14.10.3390/rs14112596 ↗
33. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance10.1016/j.rse.2016.01.023 ↗
34. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance10.1016/j.rse.2017.06.019 ↗
35. An algorithm for the retrieval of albedo from space using semiempirical BRDF models10.1109/36.841980 ↗
36. The pls Package: Principal Component and Partial Least Squares Regression in R10.18637/jss.v018.i02 ↗
37. Wang, F., Zhao, C., and Guijun, Y. (2020). Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods. Foods, 9.10.3390/foods9121778 ↗
38. Izadkhah, H. (2022). Chapter 2—A review of machine learning. Deep Learning in Bioinformatics, Academic Press.10.1016/B978-0-12-823822-6.00009-3 ↗