Biodiv Sci ›› 2020, Vol. 28 ›› Issue (4): 524-533.  DOI: 10.17520/biods.2019272

• Technology and Methodology • Previous Articles     Next Articles

Evaluation of the identification ability of eight commonly used plant identification application softwares in China

Zhanhui Xu1,Shiyao Liu1,Ying Zhao1,Wenqin Tu1,Zhaofeng Chang1,Entao Zhang1,Jing Guo1,Di Zheng1,Jun Geng1,Gaoying Gu1,Chunpeng Guo1,Lulu Guo1,Jing Wang1,Chunyang Xu1,Chuan Peng1,Teng Yang1,Mengqi Cui1,Weicheng Sun1,Jiantan Zhang1,Haotian Liu1,Chaoqun Ba1,Heqi Wang1,Jingchao Jia1,Jinzhou Wu1,Cui Xiao2,Keping Ma2,*()   

  1. 1 University of Chinese Academy of Sciences, Beijing 100049
    2 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093
  • Received:2019-09-01 Accepted:2020-01-21 Online:2020-04-20 Published:2020-06-15
  • Contact: Keping Ma


Smart phone and artificial intelligence technology development has led to various plant recognition softwares on mobile applications. These applications have gradually entered all aspects of public life, popular science activities, and scientific research activities. Presently, there are many plant recognition apps in China, which have varying development purposes and application scopes. Among these differences include variation in software concerns, database sources, algorithms, and hardware which could implicate large discrepancies between apps, making it important to analyze and evaluate the accuracy, scope of application and potential use of each software. In this paper, eight apps were selected to identify 400 accurately identified plant photos, 100 photos being chosen from arid and semi-arid zones, temperate zones, tropical zones, and subtropical zones, respectively. In total, these photos belong to 122 families, 164 genera and 340 species, covering five growth forms of trees, shrubs, herbs, herbaceous vines and woody vines, as well as 23 national protected plant species. Accurate identification of species, genera and families was scored 4, 2 and 1 points, respectively. The software recognition ability was sorted according to total scores, and the results are as follows: HuaBangZhu, Baidu-Shitu, HuaBanLv, XingSe, Huahui-Shibie, Zhiwu-Shibie, Faxian-Shihua, Flower Recognition.

Key words: Plant recognition software, HuaBangZhu, Baidu-Shitu, HuaBanLv, XingSe, Huahui-Shibie, Zhiwu-Shibie, Faxian-Shihua, Flower recognition