Moisture determination with an artificial neural network from microwave measurements on wheat

Author

Bartley, Philip G. ; McClendon, Ronald W. ; Nelson, Stuart O. ; Trabelsi, Samir

Author_Institution

Dept. of Biol. & Agric. Eng., Georgia Univ., Athens, GA, USA

Volume

2

fYear

1997

fDate

19-21 May 1997

Firstpage

1238

Abstract

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 placed between two radiating elements. The measurements were made at 8 microwave frequencies (10 to 18 GHz) on wheat samples of varying bulk densities (0.72 to 0.88 g/cm³) at 24°C. The trained network predicted moisture content (%) with a mean absolute error of 0.135

Keywords

agriculture; learning (artificial intelligence); microwave measurement; moisture measurement; neural nets; 10 to 18 GHz; 24 C; absolute error; artificial neural network; microwave measurements; moisture content; trained network; transmission coefficient measurements; wheat; Agricultural engineering; Artificial neural networks; Density measurement; Dielectric materials; Electrical resistance measurement; Electromagnetic measurements; Microwave measurements; Moisture measurement; Ovens; Permittivity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1997. IMTC/97. Proceedings. Sensing, Processing, Networking., IEEE

Conference_Location

Ottawa, Ont.

ISSN

1091-5281

Print_ISBN

0-7803-3747-6

Type

conf

DOI

10.1109/IMTC.1997.612396

Filename

612396