Title :
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
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;
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
DOI :
10.1109/TENCON.1993.320140
