Machine Recognition for Broad-Leaved Trees Based on Synthetic Features of Leaves Using Probabilistic Neural Network

This paper is to effectively solve the problem that the objects of traditional plant identification were too broad and the classification features of it were usually not synthetic and the recognition rate was always slightly low. This study gives one recognition approach, in which the shape features and the texture features of the leaves of broad-leaved trees combine, composing a synthetic feature vector of broad leaves and hoping to realize the computer automatic classification towards broad-leaved plants more convenient, rapidly and efficient. Using probabilistic neural networks (PNN), the rapid recognition for thirty kinds of broad-leaved trees was realized and the average correct recognition rate reached 98.3%. Comparison tests demonstrated that if the shape features of broad leaf solely worked as the recognition features without the texture features, the average correct recognition rate just reached 93.7%.