This paper addresses the issue of fine-grained leaf image retrieval (FGLIR) which focuses on differentiating between different leaf cultivars within the same species. We investigate a novel bag-of-visual-words approaches (BoVW) to FGLIR. Firstly, we treat each leaf boundary point as the key-point from which to spread a chord pair for measuring the local characteristics including shape, gray-level and gradient co-occurrence texture features of the leaf image. By varying the length of the chord, we obtain multiscale local features which are then used to form two local shape and texture feature vectors. Secondly, we separately collect all the local shape and texture vectors from the database images to learn a leaf shape vocabulary and a leaf texture vocabulary by k-means clustering algorithm. By mapping the two kinds of local feature vectors to visual words in their corresponding leaf vocabularies, we can represent each leaf image as two bags of visual words (one for shape, another for texture). Finally, we convert them into two visual-word vectors by counting the occurrence of each leaf visual word in the image and concatenate them as the final image representation. The proposed leaf vocabulary representation is applied to two challenging FGLIR tasks, soybean cultivar identification and peanut cultivar identification. The experimental results indicate its superior performance over the state-of-the-art leaf descriptors and show its potential to address the issue of FGLIR.