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Image-Based Plant Recognition by Fusion of Multimodal Information

Plant identification is a difficult task, even though people can seek for help from various plant illustrated books, web resources and mobile apps. This paper proposes to automatically identify the plant species by the images from different organs of one plant individual, including flowers and leaves. Flowers of different plants differ not only in colors, but also in their contours. Leaf color is usually green and un-distinguishable, but the contours of the leaves can be the identification features. We collected flower and leaf images of 50 common plants in NCNU campus, and applied circular color histogram and 1-D Fourier descriptors as color and contour features in the recognition experiments. In addition, experiments of multi-features fusion in a single organ and multi-features fusion in multiple organs were conducted by applying linear evidence combination, Dempster-Shafer evidence combination and plant information fusion. Multi-features fusion of flowers reached highest Top 10 recognition rate 87% using linear evidence combination. Applying plant information fusion, multi-features fusion of flower colors and contours and leaf contours reached Top 10 recognition rate 96.4%. In addition, a location-based search framework is proposed. A small field test of 10 queries with location information always found the correct species in the first 10 answers.