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Plant species identification is necessary for protecting biodiversity which is declining rapidly throughout the world. This research work focuses on plant species identification in simple and complex background using Computer Vision techniques. Intra-class variability and inter-class similarity are the key challenges in a large plant species dataset. In this paper, multiple organs of plants such as leaf, flower, stem, fruit, etc. are classified using hand-crafted features for identification of plant species. We propose a novel encoding scheme named as Discriminant Bag of Words (DBoW) to identify multiple organs of plants. The proposed DBoW extracts the class specific codewords, and assigns the weights to codewords in order to signify discriminant power of the codewords. We evaluated our proposed method on two publicly available datasets: Flavia and ImageClef. The experimental results achieved classification accuracy rates of 98% and 94% on FLAVIA and ImageClef datasets, respectively.

Plant Species Identification using Discriminant Bag of Words (DBoW)