How Remote Sensing Can Make Fertiliser Use Smarter, Cheaper and Greener

Authors

  • Akash Pattnaik B.Sc. Agriculture Student, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha, India

DOI:

https://doi.org/10.5281/zenodo.20567048

Keywords:

Precision agriculture, Nutrient management, Drones, Artificial intelligence, Sustainable agriculture

Abstract

Agriculture is evolving its nutrient management systems with the help of remote sensing; nutrient monitoring is now enabling farmers to detect crop stress and soil variability before it becomes apparent to the naked eye in the field. Today, using satellite imagery, drones, ground sensors, GIS, and artificial intelligence helps determine when and where nutrients are missing and how the crop is reacting. This article explains how precision nutrient management can increase fertiliser efficiency, lower input costs, enable zinc and micronutrient biofortification, and minimise environmental losses, including nutrient runoff and greenhouse gas emissions. It also emphasises the need for digital soil mapping, variable-rate fertiliser, and farmer-friendly advisory systems to make these technologies relevant to small and marginal farmers. Costs, technical skills, and model accuracy across all locations remain significant issues, but remote sensing-based nutrient management offers a viable pathway to advancing climate-smart, resource-efficient, and nutrition-sensitive agriculture. Its true value will depend on smallholder farming systems being able to access it at low cost, on extension support being available, on everyone being digitally literate, and on recommendations being localised and simplifying complex information into simple field-level decisions.

References

Badavath, A., Prusty, A. K., Meena, L. L., Pradhan, S. K., Rathna, G., & Chakraborty, S. (2024). Enhancing agriculture with digital transformation through human-computer interaction. International Journal of Agriculture Extension and Social Development, 7(4), 628–636. https://doi.org/10.33545/26180723.2024.v7.i4h.582

Chen, X., Zhang, H., & Wong, C. U. I. (2025). Dynamic monitoring and precision fertilization decision system for agricultural soil nutrients using UAV remote sensing and GIS. Agriculture, 15(15), 1627. https://doi.org/10.3390/agriculture15151627

Kumar, A., Prusty, A. K., Naik, A., Kumar, P. N., Ojha, P. K., & Mounika, T. (2025). Adoption and compliance of AI-enabled pest advisories: Evidence from the National Pest Surveillance System in Odisha, India. Indian Journal of Extension Education, 61(4), 78–83. https://doi.org/10.48165/IJEE.2025.61413

Peramaiyan, P., Craufurd, P., Kumar, V., Seelan, L. P., McDonald, A. J., Kishore, A., & Singh, S. (2022). Agronomic biofortification of zinc in rice for diminishing malnutrition in South Asia. Sustainability, 14(13), 7747. https://doi.org/10.3390/su14137747

Pradhan, S. K., Prusty, A. K., Das, S., Ghosh, M., Chandra, Y. B., & Nayak, S. (2025). Empowering small-scale agriculture: Effective strategies for technology transfer. International Journal of Advance Biochemistry Research, 9(3), 118–125. https://doi.org/10.33545/26174693.2025.v9.i3b.3902

Pradhan, S. K., Prusty, A. K., Priyadarshi, D., Badavath, A., Nayak, S., Munda, S. C., & Sudham, V. (2024). Impact of disruptive technologies on transforming Indian agriculture. International Journal of Agriculture Extension and Social Development, 7(5), 34–41. https://doi.org/10.33545/26180723.2024.v7.i5a.597

Prusty, A. K., Saha, P., Das, N., & Suman, S. (2025). Implementation and adoption of smart technologies in agri-allied sectors. Plant Science Today, 11, 3467. https://doi.org/10.14719/pst.3467

Saha, P., Prusty, A. K., & Nanda, C. (2024). Extension strategies for bridging gender digital divide. Journal of Applied Biology and Biotechnology, 12(4), 76–80. https://doi.org/10.7324/JABB.2024.159452

Samreen, T., Tahir, S., Arshad, S., Kanwal, S., Anjum, F., Nazir, M. Z., & Sidra-Tul-Muntaha. (2022). Remote sensing for precise nutrient management in agriculture. Environmental Sciences Proceedings, 23(1), 32. https://doi.org/10.3390/environsciproc2022023032

Xing, Y., Liu, X., & Wang, X. (2026). Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: Pathways to sustainable food production, resource efficiency, and scalable innovation. Frontiers in Agronomy, 7, 1670380. https://doi.org/10.3389/fagro.2025.1670380

Downloads

Published

2026-06-05

How to Cite

Pattnaik, A. (2026). How Remote Sensing Can Make Fertiliser Use Smarter, Cheaper and Greener. NG Agriculture Insights, 2(3), 15-18. https://doi.org/10.5281/zenodo.20567048

Similar Articles

1-10 of 71

You may also start an advanced similarity search for this article.