Automatic Plant Identification: Is Shape the Key Feature?

AbstractShape is the most popular feature used in plant leaf identification, be it manual or automatic plant identification. In this paper, a study is conducted to investigate the most contributing features among three low-level features for plant leaf identification. Intra- and inter-class identification are conducted using 455 herbal medicinal plant leaves, with 70% allocated for training and 30% for testing dataset. Shape feature is extracted using Scale Invariant Feature Transform (SIFT); colour is represented using colour moments; and Segmentation-Based Fractal Texture Analysis (SFTA) is utilized to describe texture feature. Intra-class analysis showed that fusion of texture and shape surpassed fusion of texture, shape and colour. Single texture feature identification also achieved highest identification rate compared to identification using colour or shape. Inter-class analysis further support texture to be the discriminative feature among the low-level features. Results demonstrate that single texture feature outperformed colour or shape feature achieving 92% identification rate. Furthermore, fusion of all three features accomplished 94% identification rate