A Multisensor Fusion System for the Detection of Plant Viruses by Combining Artificial Neural Networks

  • Dimitrios Frossyniotis
  • Yannis Anthopoulos
  • Spiros Kintzios
  • Antonis Perdikaris
  • Constantine P. Yialouris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


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).


Artificial Neural Network Plant Virus Smoothing Technique Multiple Classifier System Neural Classifier 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dimitrios Frossyniotis
    • 1
  • Yannis Anthopoulos
    • 1
  • Spiros Kintzios
    • 2
  • Antonis Perdikaris
    • 2
  • Constantine P. Yialouris
    • 1
  1. 1.Department of ScienceAgricultural University Of Athens, Informatics LaboratoryAthensGreece
  2. 2.Laboratory of Plant Physiology and Morphology, Department of Agricultural BiotechnologyAgricultural University Of AthensAthensGreece

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