Recognition of seed varieties using a temporal organisation map analysis of electrophoretic images.

This paper presents a method for seed varieties recognition using one-dimensional electrophoresis gels. It employs a neural network basically constituted of temporal organisation maps (TOM). The TOM model is a neural net which was initially developed for speech recognition. It can be trained to recognise words in speech by reference to the sound pattern over a sequence of time steps. Electrophoresis creates a set of bands in the gel, caused by migration of protein from the seed. Each seed variety generates a characteristic pattern. The bands are made visible by staining. They can then be imaged and digitised to create an input to a TOM, which treats the variation with distance along the lane in the same way as the time sequence for which it was originally employed. In this way the characteristic signature of a seed variety can be recognised. A set of 50 images — each containing 10 to 15 lanes — was used to train and test the performance of a neural network in recognising 75 cereal varieties. The network could achieve a recognition rate of 98%, provided that the gel was not distorted or cracked during heating or drying. Details of the design and training of the network are given.