• MI FAKHAR Department of Land and Water Conservation Engineering, PMAS Arid Agricultural University, Rawalpindi 46000, Pakistan
  • MN KHALID Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan



precision agriculture, remote sensing, irrigation management, sustainable farming


Precision agriculture, driven by the growing demand for sustainable farming practices, relies heavily on technologies such as remote sensing. Despite its critical role, a comprehensive review of remote sensing within the context of precision agriculture remains sparse. This paper aims to bridge this gap by providing a thorough overview of remote sensing technologies, their applications, challenges, future trends, and potential impact on precision agriculture. Employing a literature review methodology, we analyzed key studies to comprehend precision agriculture's evolution and remote sensing technologies' significant role. Our examination encompassed various remote sensing applications, from crop health monitoring to yield estimation, soil mapping, irrigation management, and pest and disease detection. We also evaluated emerging trends and identified challenges such as the need for high-resolution data, atmospheric disturbances, and requisite technical expertise for effective data interpretation. Despite these challenges, the review underscores the transformative potential of remote sensing technologies in advancing precision agriculture. Future research should prioritize addressing these challenges and strive to make these technologies more accessible and affordable. Moreover, integrating remote sensing with artificial intelligence and machine learning in interdisciplinary research could further bolster the efficacy and potential of precision agriculture.


Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of Crop Yield Forecasting Methods and Early Warning Systems. In First Meeting of the Scientific Advisory Committee of the Global Strategy to improve Agricultural and Rural Statistics, FAO Headquarters.

Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359-387.

Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science, 327(5967), 828-831.

Chen, H., Lan, Y., Fritz, B. K., Hoffmann, W. C., & Liu, S. (2021). Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Agricultural and Biological Engineering, 14(1), 38-49. DOI: 10.25165/j.ijabe.20211401.5714

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.

Khan, S. I., Khaliq, A., & Prabhakar, M. (2015). Remote Sensing and Geographical Information System Application in Irrigation Water Management. Journal of Applied and Natural Science, 7(2), 658-666.

Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782.

Li, L., Zhang, Q., & Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078-20111.

Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I., & Benning, T. L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment, 94(2), 205-220.

Mahlein, A. K. (2016). Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease, 100(2), 241-251.

Maltamo, M., Næsset, E., & Vauhkonen, J. (2014). Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies. Managing Forest Ecosystems, 27.

England, J. R., & Viscarra Rossel, R. A. (2018). Proximal sensing for soil carbon accounting. Soil, 4(2), 101-122.

Mulla, D. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.

Pôças, I., Gonçalves, P., & Pereira, L. S. (2019). NDVI from Landsat 8 Vegetation Continuous Fields: A New Approach to Normalize NDVI for the Estimation of Biophysical Parameters in Mediterranean Pear Orchards. Remote Sensing, 11(23), 2777.

Schimmelpfennig, D. (2016). Farm Profits and Adoption of Precision Agriculture. Economic Research Report, (217), 1-40.

Schimmelpfennig, D., & Ebel, R. (2011). On the doorstep of the information age: Recent adoption of precision agriculture. USDA-ERS Economic Information Bulletin, 80.

Sullivan, D. G. (2010). Hyperspectral Imaging with a Helicopter Platform: Early Detection of Plant Stress. In P. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral Remote Sensing of Vegetation (pp. 541-562). CRC Press.

Taghvaeian, S. (2015). Remote Sensing of Evapotranspiration: Theories, Models, and Applications. In Water Conservation in the 21st Century (pp. 71-98). Springer.

Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision agriculture, 13(6), 713-730.

Thenkabail, P. S. (2015). Remote Sensing Handbook - Three Volume Set: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. CRC Press.,+P.&publication_year=2018

Vasques, G. M., Grunwald, S., & Sickman, J. O. (2008). Comparison of Multivariate Methods for Inferential Modeling of Soil Carbon Using Visible/Near-Infrared Spectra. Geoderma, 146(1-2), 14-25.

Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J. P., & Veroustraete, F. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273-290.

Whelan, B., & Taylor, J. (2013). Precision Agriculture for Grain Production Systems. CSIRO Publishing.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69-80.

Zhang, N., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712.

Zhang, Z., Jayachandran, K., & Grunwald, S. (2019). Soil Information Derived from Visible/Infrared and Passive Microwave Remote Sensing: Status and Perspectives. Earth Science Reviews, 190, 420-436.




How to Cite

FAKHAR, M., & KHALID, M. (2023). SATELLITES TO AGRICULTURAL FIELDS: THE ROLE OF REMOTE SENSING IN PRECISION AGRICULTURE. Biological and Agricultural Sciences Research Journal, 2023(1), 14.

Most read articles by the same author(s)