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Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

Author

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  • Claudio Kapp Junior

    (Laboratory of Computing Applied to Agriculture, State University of Ponta Grossa, Ponta Grossa, Brazil)

  • Eduardo Fávero Caires

    (Department of Soil Science and Agricultural Engineering, State University of Ponta Grossa, Ponta Grossa, Brazil)

  • Alaine Margarete Guimarães

    (Department of Informatics, State University of Ponta Grossa, Ponta Grossa, Brazil)

Abstract

Precision Agriculture has the goal of reducing cost which is difficult when it is related to fertilizers application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizers application present significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the Resilient Propagation and Backpropagation Artificial Neural Networks algorithms. As a result was detected a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants. For both algorithms it was established a satisfactory model for estimating wheat dry biomass production. The best Backpropagation and Resilient Propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

Suggested Citation

  • Claudio Kapp Junior & Eduardo Fávero Caires & Alaine Margarete Guimarães, 2014. "Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 5(2), pages 38-49, April.
    • Handle: RePEc:igg:jaeis0:v:5:y:2014:i:2:p:38-49
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