Fig (Ficus Carica L.) Identification Based on Mutual Information and Neural Networks

Abstract

The process of recognition and identification of plant species is very time-consuming as it has been mainly carried out by botanists. The focus of computerized living plant's identification is on stable feature's extraction of plants. Leaf-based features are preferred over fru its, also the long period of its existence than fruits. In this preliminary study, we study and propose neural networks and Mutual information for identification of two, three Fig cult ivars (Ficus Carica L.) in Syria region. The identification depends on image features of Fig tree leaves. A feature extractor is designed based on Mutual Information computation. The Neural Net works is used with two hidden layers and one output layer with 3 nodes that correspond to varieties (classes) of FIG leaves. The proposal technique is a tester on a database of 84 images leaves with 28 images for each variety (class). The result shows that our technique is pro mising, where the recognition rates 100%, and 92% for the train ing and testing respectively for the two cultivars with 100% and 90 fo r the three cultivars. The preliminary results obtained indicated the technical feasibility of the proposed method, which will be applied for mo re than 80 varieties existent in Syria.