Cart (Loading....) | Create Account
Close category search window

Determining moisture content of wheat with an artificial neural network from microwave transmission measurements

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

4 Author(s)
Bartley, P.G. ; Georgia Univ., Athens, GA, USA ; Nelson, S.O. ; McClendon, R.W. ; Trabelsi, S.

An artificial neural network (ANN) was used to determine the moisture content of hard, red winter wheat. The ANN was trained to recognize moisture content in the range from 10.6% to 19.2% (wet basis) from transmission coefficient measurements on samples of wheat. The measurements were made at 8 microwave frequencies (10 GHz to 18 GHz) on wheat samples of varying bulk densities (0.72 g/cm3 to 0.88 g/cm3) at 24°C. The trained network predicted moisture content (%) with a mean absolute error of 0.135 (compared with oven-dried measurements)

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:47 ,  Issue: 1 )

Date of Publication:

Feb 1998

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.