UTILIZING SPACE IMAGERY FOR INVESTIGATING CROP VEGETATION STATUS IN PRECISION AGRICULTURE THROUGH THE GOOGLE EARTH ENGINE CLOUD PLATFORM

Authors

DOI:

https://doi.org/10.30890/2709-2313.2024-27-00-025

Keywords:

Precision Farming, Satellite Imagery, NDVI, Agricultural Land, Google Earth Engine

Abstract

This research focuses on the application of space imagery for investigating the vegetation status of crops in precision agriculture, employing the resources of the Google Earth Engine cloud platform. The study aims to assess the potential of utilizing hig

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References

Gupta, N., Gupta, P.K. (2024). Robotics and Artificial Intelligence (AI) in Agriculture with Major Emphasis on Food Crops. In: Priyadarshan, P.M., Jain, S.M., Penna, S., Al-Khayri, J.M. (eds) Digital Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-43548-5_19

Raj, V. H. A., & de Carvalho, C. X. (2023). A Perspective on the Application of Artificial Intelligence in Sustainable Agriculture with Special Reference to Precision Agriculture. SDMIMD Journal of Management, 14(Special Issue), 1–13. https://doi.org/10.18311/sdmimd/2023/33006

Ahmad, S.F., Dar, A.H. (2020). Precision Farming for Resource Use Efficiency. In: Kumar, S., Meena, R.S., Jhariya, M.K. (eds) Resources Use Efficiency in Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-15-6953-1_4

Mazzetto, F.; Gallo, R.; Sacco, P. Reflections and Methodological Proposals to Treat the Concept of “Information Precision” in Smart Agriculture Practices. Sensors 2020, 20, 2847. https://doi.org/10.3390/s20102847

Ezziyyani, M. et al. (2023). Contribution to the Development of a Technological Platform for Analysis in Precision Agriculture for the Biovigilance of Cryptogamic Diseases in Strawberry (Fragaria × ananassa). In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_57

Morota, G., Ventura, R.V., Silva, F.F., Koyama, M., Fernando, S.C.: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. J. Animal Sci. 96(4), 1540–1550 (2018)

Castle, M, Lubben, B.D., Luck, J.: Precision agriculture usage and big agriculture data (CornhuskerEconomics).http://agecon.unl.edu/cornhuskereconomics/2015/precisionagricultureusage-and-big-agriculture-data. Accessed 24 Jan 2017 (2015)

Lucas, R. M., German, S., Metternicht, G., Schmidt, R. K., Owers, C. J., Prober, S. M., Richards, A. E., Tetreault-Campbell, S., Williams, K. J., Mueller, N., Tissott, B., Chua, S. M. T., Cowood, A., Hills, T., Gunawardana, D., McIntyre, A., Chognard, S., Hurford, C., Planque, C. … Horton, C. (2022). A globally relevant change taxonomy and evidence-based change framework for land monitoring. Global Change Biology, 28, 6293–6317. https://doi.org/10.1111/gcb.16346

Cornet, V.J.; Joyce, K.E. Assessing the Potential of Remotely-Sensed Drone Spectroscopy to Determine Live Coral Cover on Heron Reef. Drones 2021, 5, 29. https://doi.org/10.3390/drones5020029

Goffart, D., Abdallah, F.B., Curnel, Y. et al. Correction to: In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data. Potato Res. 66, 1215–1223 (2023). https://doi.org/10.1007/s11540-023-09653-5

Ecke, Simon and Frey, Julian and Tiede, Dirk and Dempewolf, Jan and Klemmt, Hans-Joachim and Endres, Ewald and Seifert, Thomas and Stehr, Florian, Towards Operational Uav-Based Forest Health Monitoring: Species Identification and Crown Condition Assessment by Means of Deep Learning. Available at SSRN: https://ssrn.com/abstract=4542902

Li, Y.; Zhou, Z.; Qi, G.; Hu, G.; Zhu, Z.; Huang, X. Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism. Remote Sens. 2024, 16, 644. https://doi.org/10.3390/rs16040644

Audebert, N.; Le Saux, B.; Lefèvre, S. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 2018, 140, 20–32.

Segarra, J. (2024). Satellite Imagery in Precision Agriculture. In: Priyadarshan, P.M., Jain, S.M., Penna, S., Al-Khayri, J.M. (eds) Digital Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-43548-5_10

Adams JB, Gillespie AR (2006) Remote sensing of landscapes with spectral images: a physical modeling approach. In: Remote sensing of landscapes with spectral images: a physical modeling approach, pp 1–362. https://doi.org/10.1017/CBO9780511617195

Baumann P, Mazzetti P, Ungar J et al (2016) Big data analytics for earth sciences: the EarthServer approach. Int J Digit Earth 9(1):3–29. https://doi.org/10.1080/17538947.2014.1003106

Buchaillot ML, Cairns J, Hamadziripi E et al (2022) Regional monitoring of fall armyworm (FAW) using early warning systems. Remote Sens 14(19):5003. https://doi.org/10.3390/rs14195003

Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electron Agric 151(May):61–69. Elsevier. https://doi.org/10.1016/j.compag.2018.05.012

Gorelick N, Hancher M, Dixon M et al (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. Elsevier. https://doi.org/10.1016/J.RSE.2017.06.031

Hunt ML, Blackburn GA, Carrasco L et al (2019) High resolution wheat yield mapping using Sentinel-2. Remote Sens Environ 233(December 2018):111410. Elsevier. https://doi.org/10.1016/j.rse.2019.111410

Lambert MJ, Traoré PCS, Blaes X et al (2018) Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sens Environ 216(June):647–657. Elsevier. https://doi.org/10.1016/j.rse.2018.06.036

Inkollu, U., Sastry, J.K.R. AI-driven reinforced optimal cloud resource allocation (ROCRA) for high-speed satellite imagery data processing. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01242-5

Shrawankar, U.; Shrawankar, C. An Algorithm for High-Resolution Satellite Imagery Pre-processing. Preprints 2022, 2022030095. https://doi.org/10.20944/preprints202203.0095.v1

Published

2024-02-28

How to Cite

Chetverikov, B., Babiy, L., & Zayats, I. (2024). UTILIZING SPACE IMAGERY FOR INVESTIGATING CROP VEGETATION STATUS IN PRECISION AGRICULTURE THROUGH THE GOOGLE EARTH ENGINE CLOUD PLATFORM. European Science, 4(sge27-04), 114–138. https://doi.org/10.30890/2709-2313.2024-27-00-025