Multimedia Tools and Applications

, Volume 78, Issue 19, pp 27463–27489 | Cite as

Plant recognition via leaf shape and margin features

  • Xiang Zhang
  • Wanqing Zhao
  • Hangzai LuoEmail author
  • Long Chen
  • Jinye Peng
  • Jianping Fan


Botanists and foresters empirically determine plant categories mainly via visual features of leaves, e.g. leaf shape, leaf margin, leaf arrangement and leaf venation. The leaf shape and leaf margin can be captured easily with cheap devices. As a result, automatic plant recognition is generally based on leaf shape or margin features. In this paper, a set of features that depict leaf shape and margin are proposed to improve the performance of plant recognition. The proposed margin features utilize the area ratio to quantify the convexity/concavity of each contour point at different scales and such margin features are effective in capturing the global information and contour details. The area ratio is the ration of the disk to the inside of the contour. The proposed shape features use a combination of morphological features to characterize the global shape of the leaf, which has merits in preserving the geometric properties of leaf shape. Additionally, a series of multi-grained fusion methods that combine the margin feature and global shape feature are proposed as a better representation of a leaf. To validate the effectiveness and generalization, we evaluate our methods on two public datasets: Swedish Leaf dataset and ICL Leaf dataset. The experimental results show the superiority of our methods over state-of-the-art shape methods.


Plant recognition Leaf shape Leaf margin Feature set 



This work was supported by National Key R&D Program of China under grant no. 2018YFB0204100, National Nature Science Foundation of China under grant no. 61772419, Changjiang Scholars and Innovative Research Team in University under grant no. IRT_17R87.


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Authors and Affiliations

  1. 1.School of Information and TechnologyNorthwestern UniversityShaanxiChina
  2. 2.Department of Computer ScienceUNCCharlotteUSA

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