Automatic plant species recognition technique using machine learning approaches

Motivated from the need of automation of plant speciess recognition and availability of digital databases of plants,we propose an image based identification of speciess of plant. These images may belong to different organs of the plants such as leaf, stem or bark, flower and fruit. Different methods for recognition of the speciess are used according to the part of the plant to which the image belongs to. For flower category, fusion of shape, color and texture features are used. For other categories like stem, fruit, leaf and leafscan, Sparsely coded SIFT features pooled with Spatial pyramid matching approach is used. To cater the seasonal and topographic influences on the appearance of the plant, our system also uses metadata i.e. content, date, time, latitude, longitude associated with images to aid the identification process and obtain more accurate results. For a given image of plant and associated metadata, the system recognizes the speciess of the given plant image and produces an output that contains the Family, Genus, and Speciess name. The proposed framework is implemented and tested on ImageClef data with 50 different classes of speciess. Maximum accuracy of 98% is attained in leaf scan sub-category whereas minimum accuracy is achieved in fruit sub-category which is 67.3 %.