Chandrasekaran et al. Res. Trends Int. J. Technol. Innov., July - September 2026, 1 (3) : 42-49
1Department of Soil Science, Tamil Nadu Agricultural University, Coimbatore, India; 2Department of Computer Science, PSG College of Technology, Coimbatore, India
Article History
Accepted : 01 Jul 2026
Published : 10 Jul 2026
Publication Issue
Volume 1, Issue 3
July - September 2026
Page Number42–49
Uniform blanket fertiliser application ignores within-field variability in soil nutrient status, contributing to both over-application costs and yield-limiting deficiencies. This paper describes an IoT sensor grid measuring soil nitrogen, phosphorus, potassium and pH at 20 points across a 6-hectare field, feeding a regression model that generates site-specific fertiliser recommendations mapped to a georeferenced grid. Farmer trial plots using the recommendation map reduced fertiliser expenditure by 19 percent while increasing yield by 7 percent relative to uniform blanket application on adjacent control plots.
Keywords - IoT, soil nutrient monitoring, precision agriculture, site-specific fertilization, NPK sensing
Blanket fertiliser recommendations derived from a single composite soil sample per field mask substantial within-field spatial variability in nutrient status, which precision agriculture approaches aim to address through denser, georeferenced sampling and variable-rate application.
Twenty low-cost NPK and pH sensor nodes were deployed on a grid across a 6-hectare field and interfaced to a central logger via LoRa, with readings combined with historical yield-map data in a random-forest regression model to generate a georeferenced fertiliser prescription map at 20m resolution, evaluated against uniform blanket application on an adjacent control area of equivalent size and crop.
Trial plots following the site-specific recommendation map reduced total fertiliser expenditure by 19 percent while achieving a 7 percent yield increase relative to the uniformly fertilised control plots, attributed to correcting localised potassium deficiency identified in the eastern third of the trial field that blanket sampling had not detected.
Dense IoT-based soil nutrient sensing combined with site-specific prescription mapping can simultaneously reduce input costs and improve yield relative to blanket fertilisation. Future work will integrate real-time in-season sap-testing sensors to refine top-dressing recommendations.
[1] Mulla D. J., Twenty five years of remote sensing in precision agriculture, Biosystems Engineering, 2013. [2] Adamchuk V. I. et al., On-the-go soil sensors for precision agriculture, Computers and Electronics in Agriculture, 2004.
© 2026 The Author(s). Published by IJEIA Editorial Office. This is an open access article under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Balamurugan Chandrasekaran, Revathi Suresh (2026). IoT-Enabled Soil Nutrient Monitoring System for Site-Specific Fertilizer Recommendation. IJEIA, 1(3), 42-49.