Multi-organ plant identification with multi-column deep convolutional neural networks

Automatically identifying plants from images is a hot research topic due to its importance in production and science popularization. This process attempts to automatically identify the name of a plant with a known taxon from a given image. The majority of existing studies on automatic plant identification focus on identifying plants with a single organ, such as flower, leaf, or fruits. Plant identification using a single organ is not sufficiently reliable because different plants many have similar organs. To overcome this problem, this paper is devoted to automatically identifying plants by combining multiple organs of plants. Specifically, we propose a multi-column deep convolutional neural networks (MCDCNN) model to combine multiple organs for efficient plant identification. Extensive experiments demonstrate the effectiveness of our model, and the plant identification performance is greatly improved.