Intelligent Agriculture

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Soil Nutrient Assessment Using Ion-Selective Electrode-Based Nutrient Analyzer for Precision Agriculture

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https://doi.org/10.54963/ia.v2i1.2334
Volume 2 Issue 1 (2026): In Progress
Copyright (c) 2026 Preity Mishra, Swades Kumar Chaulya, Anubhuti Kumari, Naresh Kumar, Vikash Kumar, Vijay Kumar Rawani
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Authors

  • Preity Mishra

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India
  • Swades Kumar Chaulya

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India
  • Anubhuti Kumari

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India
  • Naresh Kumar

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India
  • Vikash Kumar

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India
  • Vijay Kumar Rawani

    CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826001, India

Received: 8 November 2025; Revised: 28 December 2025; Accepted: 29 January 2026; Published: 24 February 2026

Rapid and accurate soil nutrient assessment is critical for precision agriculture. This study presents a portable and intelligent soil nutrient analyzer based on ion-selective electrodes (ISE) for rapid, on-site estimation of potassium, nitrate, and chloride. Unlike image-based or machine learning approaches that rely on indirect inference, the proposed system directly measures ion activity using electrochemical sensing, ensuring higher reliability under field conditions. The device integrates sensing, signal conditioning, self-calibration using polynomial regression, and wireless data transmission for real-time soil health assessment. A total of 546 soil samples collected from diverse agricultural locations in Dhanbad district, India, were used for validation, with measurements compared against standard laboratory methods including flame photometry and UV-Vis (ultraviolet-visible) spectrophotometry. The developed system achieved high correlation coefficients of 0.994 (potassium), 0.933 (nitrate), and 0.946 (chloride). Statistical evaluation using RMSE (root mean square error), measurement uncertainty, and hypothesis testing confirms the robustness of the calibration model. The study highlights the advantages of direct sensing over image-based prediction methods, particularly in terms of accuracy, environmental robustness, and practical deployment. Limitations related to sensor drift, soil heterogeneity, and field conditions are also discussed. The proposed system provides a scalable and cost-effective solution for precision agriculture and real-time soil monitoring.

Keywords:

Soil Nutrient Analysis Ion-Selective Electrodes Precision Agriculture Soil Nutrient Prediction

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