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In few last years, deep learning has itself set up as the new strategy for plant classification. Deep learning has a best performance for object recognition. In this paper, we have focused on the case of Morocco aromatic and medicinal plant (AMP) classification. Leaf is an important organ of plant, it has shown satisfying performances for plant classification and recognition In addition to leaves, we have used an others organs of AMP.ie. leaf veins and branches. We have proposed a new model combining dynamically the CNN classification result using the entropy impurity method. In the experiments, we have used VGG16, ResNet50 and Inception V3 CNN models. We have used Keras with Tensorflow backend to build and compile all neural network models. The experiments present that our model shown the higher classification accuracy.

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In this work, we propose an image search method by visual content (CBIR), which is based on the color descriptor. The proposed method take account the spatial distribution of colors and make the signature partially invariant under rotation. The basic idea of our method is to use circular shift (clockwise or anticlockwise direction) and mirror (horizontal direction and vertical direction respectively) matching scheme to measure the distance between signatures. Through some experiments, we show that this approach leads to a significant improvement in the quality of results.

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