Abstract
The growing trend in the world population causes an increment in the demand for food as fruits like oranges. In this context, crop grass coverage becomes essential to reduce the resources in tree maintenance and improve the harvest. In this paper, we propose the use of remote sensing for monitoring the grass coverage. To do so, we have compared in times between plots with and without initial coverage. In our work, we present image-processing techniques that consist of using different bands of Sentinel-2 images for different periods of the year, of obtaining reliable information for changes in the grass-coverage of the selected plots. The pixels of the selected images have a resolution of 10 m × 10 m wherein our experiment represents information about orange trees plus grass coverage or soil. In addition, we present the results of the study, demonstrating the best behaviour of the presented technique. For this experiment, we use five different brands of the satellite: red band, green band, blue band, near-infrared band, and water vapour band. As well as normalised vegetation index using combinations of red and infrared bands. The significance values are obtained applying Single Analysis of Variance, a Statistical analysis. In this case, the higher results are located in WVP band with F- Reason of 42.56 and P-Value of 0.000 blue bands with F-Reason of 38.61 and P-Value of 0.000.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Prosekov, A.Y., Ivanova, S.A.: Providing food security in the existing tendencies of population growth and political and economic instability in the world. Foods Raw Mater. 4(2) (2016)
Narvaez, F.Y., Reina, G., Torres-Torriti, M., Kantor, G., Cheein, F.A.: A survey of ranging and imaging techniques for precision agriculture phenotyping. IEEE/ASME Trans. Mechatron. 22(6), 2428–2439 (2017)
Mahlein, A.K.: Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016)
Parra, L., Parra, M., Torices, V., Marín, J., Mauri, P., Lloret, J.: Comparison of single image processing techniques and their combination for detection of weed in Lawns. Int. J. Adv. Intell. Syst. 12(3–4), 177–190 (2019)
Parra, M., Parra, L., Mostaza-Colado, D., Mauri, P., Lloret, J.: Using satellite imagery and vegetation indices to monitor and quantify the performance of different varieties of Camelina Sativa. In: GEOProcessing 2020 The Twelfth International Conference on Advanced Geographic Information Systems, Applications, and Services. IARIA, Valencia, Spain, pp. 42–47 (2020)
Tokekar, P., Vander Hook, J., Mulla, D., Isler, V.: Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans. Rob. 32(6), 1498–1511 (2016)
Sozzi, M., Marinello, F., Pezzuolo, A., Sartori, L.: Benchmark of satellites image services for precision agricultural use. In Proceedings of the AgEng Conference, AgEng, Wageningen, The Netherlands, pp. 8–11 (2018)
Sagawa, T., Yamashita, Y., Okumura, T., Yamanokuchi, T.: Shallow water bathymetry derived by machine learning and multitemporal satellite images. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 8222–8225. IEEE (2019)
Zhang, H., Roy, D.P., Yan, L., Li, Z., Huang, H.: Comparison of Landsat-8 and Sentinel-2A reflectance and normalised difference vegetation index. In: AGUFM 2017, vol 2017 EP23B–1919
Gutierrez, S.K., Morales, R.O., Díaz, O.D.L.C.A., Ruiz, E.P.: Multispectral aerial image processing system for precision agriculture. Sistemas Telemática, 16(47) (2018)
Müllerová, J., Brůna, J., Dvořák, P., Bartalo, T., Vítková, M.: Does the data resolution/origin matter? Satellite, airborne and uav imagery to tackle plant invasions. In: International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 41 (2016)
Ezenne, G.I., Jupp, L., Mantel, S.K., Tanner, J.L.: Current and potential capabilities of UAS for crop water productivity in precision agriculture. Agric. Water Manag. 218, 158–164 (2019)
Taha, M., Parra, L., Garcia, L., Lloret, J. An Intelligent handover process algorithm in 5G networks: the use case of mobile cameras for environmental surveillance. In 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France, , pp. 840–844. IEEE (2017)
Sentinel ESA. https://sentinel.esa.int/web/sentinel/missions/sentinel-2. Accessed 25 May 2020
ArcGis Software. https://www.arcgis.com/index.html. Accessed 19 Feb 2021
STATGRAPHICS Centurion XVIII Software. https://statgraphics.net/descargas/. Accessed 28 May 2020
Acknowledgement
This work has been partially funded by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3–227 SMARTWATIR, and by Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Basterrechea, D.A., Parra, L., Parra, M., Lloret, J. (2021). A Proposal for Monitoring Grass Coverage in Citrus Crops Applying Time Series Analysis in Sentinel-2 Bands. In: Peñalver, L., Parra, L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-71061-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-71061-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71060-6
Online ISBN: 978-3-030-71061-3
eBook Packages: Computer ScienceComputer Science (R0)