The feature extraction of leaf serration plays a crucial role in studying the plant gene relationship. To overcome the drawbacks of current algorithms, a novel leaf feature extraction algorithm based on bounded variation is proposed. Thereby, multi-parameters such as serration numbers, serration depth, and serration width can be obtained. These parameters provide an important basis for the subsequent gene analysis. Firstly, the boundary coordinates of a leaf are obtained based on image preprocessing. Secondly, we calculate the bounded variation between the adjacent pixels of the boundary. As such the initial values of the corner points can be obtained. Thirdly, the values of serration depth are computed for error compensation, and the coordinates of corner points (top and bottom) are obtained. Finally, all kinds of features, including serration numbers, serration depth, and serration width, are calculated. A big aspen dataset (12 000 pieces) collected from real world are used for algorithm validation. The results show that the estimation accuracy of serration numbers achieves 86.3% and all the parameters shown above can be batch extracted.