By Topic

An artificial neural network for classifying and predicting soil moisture and temperature using Levenberg-Marquardt algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Atluri, V. ; Dept. of Math. & Comput. Sci., Alabama A&M Univ., Normal, AL, USA ; Chih-Cheng Hung ; Coleman, T.L.

The purpose of this study was to design an artificial neural network that classifies soils and quantitatively predict the soil moisture and temperature in a given soil type based on the remotely sensed data. Two different training algorithms, viz., backpropagation (BP) and Levenberg-Marquardt (LM), were employed. The accuracy of the networks studied ranged from 96.68 to 98.8%. The networks trained with LM algorithm were faster. It is concluded that neural networks can be used as a paradigm in soil classification as well as in predicting the quantity of soil moisture and temperature accurately, using remotely sensed microwave data, and thus helps achieve a proper crop management

Published in:

Southeastcon '99. Proceedings. IEEE

Date of Conference:

1999