The application of artificial neural networks to the classification of Australian wheat varieties

Author

Fung, C.C. ; Vuori, T.A. ; Belford, N.R. ; Fakhri, W.A. ; Myers, D.G.

Author_Institution

Curtin Univ. of Technol., Perth, WA, Australia

Volume

2

fYear

1993

fDate

19-21 Oct. 1993

Firstpage

822

Abstract

Reports results obtained from the application of artificial neural networks to an Australian wheat variety classification problem. A ´HyperSAB´ (Hyper-Self-Adaptive Backpropagation) network with a self-adaptive acceleration strategy for the error backpropagation learning algorithm has been developed. This has been applied to six different Australian wheat varieties with 200 samples in each case. The results indicate that the artificial neural network has some potential to be used as an identification tool in this problem.<>

Keywords

agriculture; backpropagation; biology computing; neural nets; pattern recognition; self-adjusting systems; Australian wheat variety classification; HyperSAB network; artificial neural networks; error backpropagation learning algorithm; hyper-self-adaptive backpropagation network; identification tool; self-adaptive acceleration strategy; Artificial neural networks; Australia; Chemical analysis; Feeds; Image processing; Neurons; Pattern recognition; Quality assurance; Statistical analysis; Throughput;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on

Conference_Location

Beijing, China

Print_ISBN

0-7803-1233-3

Type

conf

DOI

10.1109/TENCON.1993.320140

Filename

320140