Residual soil nitrate prediction from imagery and non-imagery information using neural network technique

Ramesh Gautam, Suranjan Panigrahi, David Franzen, Albert Sims

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Textural features extracted from LANDSAT satellite image and non-imagery information like soil electrical conductivity, crop yield, topography, and crop dry residue matter etc., were used to develop residual soil nitrate prediction models using three neural networks; back propagation, modular, and radial basis function architectures. Statistical parameters were compared to evaluate the performance of three neural network models. The residual soil nitrate prediction model based on back propagation neural network (BPNN) architecture depicted the highest average accuracy of 83.29% and the lowest root mean square error of 10.61%. The corresponding correlation coefficient of 91% was the highest among those provided by all three NN models. Sensitivity analysis showed equal importance of both imagery and non-imagery variables for predicting residual soil nitrate content in field conditions.

Original languageEnglish (US)
Pages (from-to)20-28
Number of pages9
JournalBiosystems Engineering
Volume110
Issue number1
DOIs
StatePublished - Sep 2011

Bibliographical note

Funding Information:
The authors appreciate Agri-Images Inc, Fargo for providing satellite images and USDA-IFAFS program for financial support of this study.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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