Plant identification based on very deep convolutional neural networks

Abstract

Plant identification is a critical step in protecting plant diversity. However, many existing identification systems prohibitively rely on hand-crafted features for plant species identification. In this paper, a deep learning method is employed to extract discriminative features from plant images along with a linear SVM for plant identification. To offer a self-learning feature representation for different plant organs, we choose a very deep convolutional neural networks (CNNs), which consists of sixteen convolutional layers followed by three Fully-Connected (FC) layers and a final soft-max layer. Five max-pooling layers are performed over a 2?2 pixel window with stride 2. Extensive experiments on several plant datasets demonstrate the remarkable performance of the very deep neural network compared to the hand-crafted features.