Artificial Neural Network Selection for the Detection of Plant Viruses

The aim of this work is the development of an intelligent system for the detection of plant viruses, using biosensors and Artificial Neural Networks. 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 its identification. Currently this is achieved empirically by examining the biosensor’s response data curve. In this paper, we used specialized Artificial Neural Networks that were trained to recognize plant viruses according to biosensors’ responses. Moreover, in order to increase the stability and the generalization capability of the classification model we applied a smoothing technique of the data. In addition, we used an advanced energy function for the training of the ANN network to reduce the complexity of the model.