A Cost Effective and Field Deployable System for Soil Macronutrient Analysis Based on Near-Infrared Reflectance Spectroscopy

  • Priya Sharma
  • Nirmalya Samanta
  • Shyamal Gan
  • Durga Bhattacharyya
  • Chirasree RoyChauduriEmail author
Conference paper
Part of the Design Science and Innovation book series (DSI)


Near-infrared reflectance (NIR) spectroscopy is a technique that shows many possibilities in the field of testing chemical and physical properties of soil. This paper is aimed to design a prototype of an integrated optoelectronic sensing system capable of estimating soil macronutrients- Nitrogen (N), Phosphorus (P), Potassium (K), pH and Organic Carbon (OC) which play major role in the process of plant growth. 24 soil samples have been collected from farming fields of Birbhum, West Bengal and calibrated using near infrared reflectance spectroscopy. Partial least square (PLS) model along with Standard Normal Variate (SNV) pre-treatment technique has been developed to extract features from the spectroscopic plot which is required for development of field deployable system. The handheld interface attempted in this paper comprises of a simple soil sieve, the output of which is fed in the optoelectronic system which exposes the sieved soil sample to selected IR wavelengths and processes the photodiode output using ARDUINO microcontroller. The trained PLS model embedded in the microcontroller provides estimation of the essential macronutrients with appreciable accuracy. This indigenous system is expected to enhance the scope of precision agriculture in rural India.


Near infrared (NIR) spectroscopy NPK OC pH Partial least squares regression (PLSR) Root mean squares error (RMSE) Standard normal variate (SNV) Optoelectronic sensing system Field deployable 



We would like to acknowledge Prof. Rajib Bandyopadhyay and Mr. Somdeb Chanda, Department of Instrumentation and Electronics of Jadavpur University for extending their support in conducting the NIR measurements for calibration. We are grateful to Prof. S. Bhaumik of IIEST Shibpur, who is heading the rural technology project in the Institute for providing the necessary financial assistance.


  1. 1.
    Toth B, Mako A, Gergely TO (2014) Role of soil properties in water retention characteristics of main Hungarian soil types. J Cent Eur Agric 15:137–153CrossRefGoogle Scholar
  2. 2.
    Joseph V (2010) Sinfield, Daniel Fagerman, Oliver Colic, Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils. Comput Electron Agric 70(1):1–18MathSciNetCrossRefGoogle Scholar
  3. 3.
    Martens H, Naes T (1989) Multivariate calibration. Wiley, Chichester, p 419Google Scholar
  4. 4.
    Vasques GM, Grunwald S, Sickman JO (2008) Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146:14–25CrossRefGoogle Scholar
  5. 5.
    Shi T, Cui L, Wang J, Fei T, Chen Y, Wu G (2012) Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Springer Science Business Media B.V., pp 363–375Google Scholar
  6. 6.
    He Yong, Huang Min, Garc’ia Annia, Hern’andez Antihus, Song Haiyan (2007) Prediction of soil macronutrients content using near-infrared spectroscopy. Comput Electron Agric 58:144–153CrossRefGoogle Scholar
  7. 7.
    Dale (2012) Chemometrics tools for NIRS and NIR HSI Review I. Bull UASMV Cluj Agric 69(1):70–76Google Scholar
  8. 8.
    Mohapatra AG, Lenka SK (2015) Sensor system technology for soil parameter sensing in precision agriculture: a review. J Agric Phys 15(2):181–202Google Scholar
  9. 9.
    Treiman AH, Shelfer TD (2000) Manually portable reflectance spectrometer, US Patent 6043893A, pp 1–10Google Scholar
  10. 10.
    Westerman RL, Baird JV, Christensen NW, Fixen PE, Whitney DA (1990) Soil-testing and plant analysis, 3 edn. Soil Science Society of America Book Series, pp 73–228Google Scholar
  11. 11.
    Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44:71–91CrossRefGoogle Scholar
  12. 12.
    Shibusawa S, Sato H, Hirako S, Otomo A, Sasao A (2000) A revised soil spectrophotometer. In: Proceedings of 2nd IFAC/CIGR international workshop on biorobotics II, pp 225–230CrossRefGoogle Scholar
  13. 13.
    Liu W, Upadahyaya SK, Kataoka T, Shibusawa S (1996) Development of a texture/soil compaction sensor. In: Proceedings of the Third International Conference on Precision Agriculture. ASA-CSSA-SSSA, pp 617–630Google Scholar
  14. 14.
    An X, Li M, Zheng L, Liu Y, Sun H (2014) A portable soil nitrogen detector based on NIRS. Precis Agric 15:3–16. Springer Science Business Media, New York, pp 3–16Google Scholar
  15. 15.
    Lee WS, Bogrekci I (2007) Portable raman sensor for soil nutrient detection, US Patent 2007/0013908 A1, pp 1–12Google Scholar
  16. 16.
    Holland KH (2013) Optical real-time soil sensor, US Patent 8,451,449 B2, pp 1–18Google Scholar
  17. 17.
    Stone ML, Needham D, Solie JB, Raun WR, Johnson GV (2005) Optical spectral reflectance sensor and controller, US. Patent, pp 1–16Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Priya Sharma
    • 1
  • Nirmalya Samanta
    • 1
  • Shyamal Gan
    • 2
  • Durga Bhattacharyya
    • 2
  • Chirasree RoyChauduri
    • 1
    Email author
  1. 1.Department of Electronics and Telecommunication EngineeringIndian Institute of Engineering, Science and TechnologyShibpurIndia
  2. 2.Loka Kalyan ParishadKolkataIndia

Personalised recommendations