Hybrid Consensus Learning for Legume Species and Cultivars Classification

In this work we propose an automatic method aimed at classifying five legume species and varieties using leaf venation features. Firstly, we segment the leaf veins and measure several multiscale morphological features on the vein segments and the areoles. Next, we build a hybrid consensus of experts formed by five different automatic classifiers to perform the classification using the extracted features. We propose to use two strategies in order to assign the importance to the votes of the algorithms in the consensus. The first one is considering all the algorithms equally important. The second one is based on the accuracy of the standalone classifiers. The performance of both consensus classifiers show to outperform the standalone classification algorithms in the five class recognition task.