Call for Paper - January 2021 Edition
IJCA solicits original research papers for the January 2021 Edition. Last date of manuscript submission is December 21, 2020. Read More

An Empirical Exponential Model based on Reflectivity Measurements for Soil Nitrogen Detection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Deepti Lourembam, Subra Mukherjee

Deepti Lourembam and Subra Mukherjee. An Empirical Exponential Model based on Reflectivity Measurements for Soil Nitrogen Detection. International Journal of Computer Applications 172(6):21-25, August 2017. BibTeX

	author = {Deepti Lourembam and Subra Mukherjee},
	title = {An Empirical Exponential Model based on Reflectivity Measurements for Soil Nitrogen Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {6},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {21-25},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017915163},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Right amount of nutrients in soil plays an important role in maintaining the health of the plant as well as the soil. In this regard, developing novel ways of soil macronutrient detection has become a must in the present day scenario. Studies based on diffused reflectance for detecting soil nutrients and attributes have taken great strides towards building up more effective systems. These trends have been contributing significantly to precision farming. Reflectance measurements in the near-infrared region have proved to be reliable in terms of measuring various soil attributes like pH, organic matter and soil nutrients. In the current work, a system was designed using an NIR source of 850nm to detect the amount of nitrogen present in soil. It was used to collect the reflectivity of the sample when variable amount of chemical was added which was then correlated to the output voltage. Based on the raw data collected, a mathematical model was developed through statistical analysis. The exponential based model was found to have the best fit with the characteristic data obtained from the sensor when analyzed statistically. The model performance estimated for the optimized combination were R2 of 0.99 and RMSE of 0.5. Moreover, an algorithm based on this model was tested and validated with a success rate of 90%. Based on the thresholds obtained from experimentation, an Arduino was programmed in order to detect the presence of nitrogen in soil as low, medium and high. The system so designed can be employed as a cost-effective optical sensor for detection of soil nitrogen and can make a significant impact on precision farming.


  1. Bah, A and S.K. Balasundram, “Sensor Technologies for Precision Soil Nutrient Management and Monitoring”, 2012, American Journal of Agricultural and Biological Sciences 7 (1): 43-49 ISSN 1557-4989
  2. V.I Adamchuk, J.W Hummel, M.T Morgan, S.K Upadhyaya, “On-the-go soil sensors for precision agriculture”, Computers and Electronics in Agriculture, Volume 44, Issue 1, July 2004, Pages 71-91,ISSN01681699, 000444)
  3. J. A. Thomasson, R. Sui, M. S. Cox, A. Al Rajehy , “Soil Reflectance Sensing For Determining Soil Properties In Precision Agriculture” , Transactions of the ASAE,Vol. 44(6) pages: 1445–1453 @2001 American Society of Agricultural Engineers ISSN 0001–235
  4. Yubing Wang, Tianyu Huang, Jing Liu, Zhidan Lin, Shanhong Li, Rujing Wang, Yunjian Ge, “Soil pH value, organic matter and macronutrients contents prediction using optical diffuse reflectance spectroscopy”, Computers and Electronics in Agriculture, 111 (2015) Pages: 69–77. ©Elsevier BV
  5. Yandan Qiao and Shujuan Zhang, “Near-infrared spectroscopy technology for Soil nutrients detection based on LS-SVM”, CCTA (1), volume 368 of IFIP Advances in Information and Communication Technology, page: 325-335. Springer, (2011)
  6. Michael Schirrmann, Robin Gebbers, Eckart Kramer and Jan Seidel, “Soil pH mapping with an On-The-Go Sensor”, Sensors 2011, 11, pages: 573-598; doi: 10.3390/s110100573
  7. Flávio Anastácio de Oliveira Camargo, Clesio Gianello, Marino José Tedesco, João Riboldi ,Egon José Meurer ,Carlos Alberto Bissani, “Empirical models to predict soil nitrogen mineralization”, ISSN 0103-8478, Ciência Rural, Santa Maria, v.32, n.3, pages:93-399, 2002
  8. Siti Jahara Matlan, Muhammad Mukhlisin and Mohd Raihan Taha ,“Performance Evaluation of Four-Parameter Models of the Soil-Water Characteristic Curve”, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2014, Article ID 569851, 12 pages
  9. Sophie Fabre, Xavier Briottet and Audrey Lesaignoux, “Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4–2.5 µm Domain”, Sensors 2015, 15, pp:3262-3281; doi:10.3390/s150203262, ISSN 1424-8220,
  10. Lanfa Liu, Min Ji, Yunyun Dong, Rongchung Zhang and Manfred Buchroithne ,“Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction”, Remote Sens. 2016, 8, 1035; doi:10.3390/rs8121035
  11. Marek Wójtowicz ,Andrzej Wójtowicz ,Jan Piekarczyk ,“Application of remote sensing methods in agriculture”, International Journal Of The Faculty Of Agriculture And Biology, Warsaw University Of Life Sciences ,Sggw, Poland, Vol. 11, No. 1, 2016, Pp. 31–50 @CBCS
  12. Jardes Bragagnolo et al., “Optical crop sensor for variable-rate nitrogen fertilization in Corn: I-plant nutrition and dry matter production”, 2013, R. Bras. Ci solo, pages: 1288-1298
  13. T.N. Nath, “Status of Macronutrients (N, P And K) In Some Selected Tea Growing Soils Of Sivasagar District Of Assam, India ” ,International Research Journal Of Chemistry (IRJC),ISSN,pages:2321 – 2845(Online), 2321 – 3299 (Print)
  14. Siti Noor Aliah Baharom, Sakae Shibusawa, Maskazu kodaira and Ryuhei Kanda , “Multiple-depth mapping of soil properties using visible and near infrared real time soil sensor for a paddy field”, Engineering in Agriculture, Environment and Food ,8(2015) pages:13-17 ©Elsevier.
  15. R. Sui, J. A. Thomasson, “Ground-Based Sensing System for Cotton Nitrogen Status Determination, Transactions of the ASABE Vol. 49(6) pages: 1983−1991 @ 2006 American Society of Agricultural and Biological Engineers ISSN 0001−2351.


Precision farming, mathematical modeling, Nitrogen, Sensors, Reflectivity, Soil Nutrients.