Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination

The feed-forward neural network has become popular as a classification method in agricultural engineering as well as in other applications. This is despite the fact that statistically based alternatives have been in existence for a considerable time. This paper compares a Bayesian classifier with a multilayer feed-forward neural network in a task from the area of discriminating plants, weeds, and soil in colour images. The principles behind and the practical implementation of Bayesian classifiers and neural networks are discussed as are the advantages and problems of each. Experimental tests are conducted using the same set of training and test data for each classifier. Because the Bayesian classifier is optimal in the sense of total misclassification error, it should outperform the neural network. It is shown that this is generally the case. There are significant similarities in the performance of each classifier. Understanding why this should be the case gives insight into the operation of each classifier and so the paper explores this aspect. In this work, the Bayesian classifier is implemented as a look-up table. Thus any probability function can be represented and the decision surfaces can be of any shape, i.e. the classifier is not restricted to a linear form. On the other hand, it does require a relatively large amount of memory. However, memory requirement is no longer such a major issue in modern computing. Thus, it is concluded that if the number of features is small enough to require a feasible amount of storage, a Bayesian classifier is preferred over a feed-forward neural network.