Modeling in Nutrient Sensing for Agricultural and Environmental Applications

  • Won Suk Lee
  • Ismail Bogrekci
  • Min Min
Part of the Springer Optimization and Its Applications book series (SOIA, volume 25)


This chapter describes applications of modeling in nutrient prediction, such as nitrogen (N) for citrus production and phosphorus (P) for agricultural and environmental purposes. Heavy reliance on agricultural chemicals has raised many environmental and economic concerns. Some of the environmental concerns include the presence of agricultural chemicals in groundwater and eutrophication in lakes due to excessive nutrients. To prevent groundwater contamination or eutrophication in lakes, excess use of chemicals should be avoided. Timely and efficient supplies of nutrients for agricultural production are also essential for high yield and profit.

Nitrogen is an essential nutrient for growing crops and is also a concern in maintaining a healthy environment. It is well-known that excess P entering a lake from surrounding agricultural fields causes many problems, such as periodic algal blooms and displacement of native ecosystems. Currently, N and P concentrations are measured from samples obtained in agricultural fields through standard laboratory analysis procedures, which are very time consuming, costly, and labor intensive.

Real-time sensing systems using N and P prediction models will enable cost-effective nutrient detection, which will greatly decrease the time and labor required for monitoring nutrient levels in crops and in tributaries of lakes. Citrus tissue samples are acquired from commercial groves at different times of the year and at different stages of growth. Soil samples are obtained from different locations in drainage basins of lakes. Reflectance spectra of samples are measured in the ultraviolet, visible, and near-infrared regions. Nutrient concentrations in the samples are correlated with the absorbance of the same samples.

Prediction models are developed using different statistical methods, such as stepwise multiple linear regression (SMLR) and partial least squares (PLS) regression. Then, N and P concentrations in unknown samples are determined nondestructively from reflectance spectra of the samples. Such prediction could be used to better assess the effectiveness of best management practices for fertilizers. The sensor systems are combined with a differential Global Positioning System (DGPS) receiver, and they can generate a nutrient concentration map of the entire citrus grove or lake drainage basin under investigation.


Partial Little Square Partial Little Square Regression Stepwise Multiple Linear Regression Partial Little Square Analysis Differential Global Position System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Won Suk Lee
    • 1
  • Ismail Bogrekci
  • Min Min
  1. 1.Department of Agricultural and Biological EngineeringUniversity of FloridaGainesvilleUSA

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