Plant recognition based on Jaccard distance and BOW

Plant recognition is a meaningful research that has attracted many researchers. Due to the variety of plants, it is difficult for the existing identification methods to identify their species efficiently. We proposes a plant recognition method based on Jaccard distance and Bag of words (BOW). Firstly, Jaccard distance is employed to calculate the similarity between the test sample and part of the training samples of all species, $$C_{1}$$ species with the highest similarity are selected as candidate species of the test image, which not only reduce the amount of computation but also shorten the time consumption. Secondly, BOW is employed to extract features from texture image and contour image, and support vector machine is used for training and classification. In our method, the texture and contour features of leaf images are extracted by Laws texture measure and Sobel operators respectively. The local and global features of the leaf can be described well. Some representative datasets are used to evaluate the proposed method and obtain high accuracy. Comparison with existing methods proves that the proposed method not only has a high accuracy, but also has robustness in noise environment.