A multisensor fusion system for the detection of plant viruses by combining artificial neural networks

Authors:
Dimitrios Frossyniotis;Yannis Anthopoulos;Spiros Kintzios;Antonis Perdikaris;Constantine P. Yialouris
Affiliations:
Department of Science, Agricultural University Of Athens, Informatics Laboratory, Athens, Greece;Department of Science, Agricultural University Of Athens, Informatics Laboratory, Athens, Greece;Laboratory of Plant Physiology and Morphology, Department of Agricultural Biotechnology, Agricultural University Of Athens, Athens, Greece;Laboratory of Plant Physiology and Morphology, Department of Agricultural Biotechnology, Agricultural University Of Athens, Athens, Greece;Department of Science, Agricultural University Of Athens, Informatics Laboratory, Athens, Greece
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a multi-net system for the detection of plant viruses, using biosensors. The system is based on the Bioelectric Recognition Assay (BERA) method for the detection of viruses, developed by our team. BERA sensors detect the electric response of culture cells suspended in a gel matrix, as a result to their interaction with virus's cells, rendering thus feasible his identification. Currently this is achieved empirically by examining the biosensor's response data curve. In this paper, we use a combination of specialized Artificial Neural Networks that are trained to recognize plant viruses according to biosensors' responses. Experiments indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).