Title :
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
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;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1997. IMTC/97. Proceedings. Sensing, Processing, Networking., IEEE
Conference_Location :
Ottawa, Ont.
Print_ISBN :
0-7803-3747-6
DOI :
10.1109/IMTC.1997.612396
