北方草地植物物种智能识别模型构建及应用

doi:10.11983/CBB24027

植物学报 ›› 2025, Vol. 60 ›› Issue (1): 74-80.DOI: 10.11983/CBB24027 cstr: 32102.14.CBB24027

北方草地植物物种智能识别模型构建及应用

宣晶¹^,²^,^†, 付其迪¹^,^†, 谢淦¹^,²^,^†, 薛凯¹, 雒海瑞¹^,², 魏泽¹, 赵明月¹, 智亮¹, 万华伟³, 高吉喜³, 李敏¹^,²^,^*()

¹中国科学院植物研究所, 大数据与AI生物多样性保护研究中心, 北京 100093
²国家植物园, 北京 100093
³生态环境部卫星环境应用中心, 北京 100094

收稿日期:2024-02-23 接受日期:2024-06-21 出版日期:2025-01-10 发布日期:2024-06-24
通讯作者: * 李敏, 高级工程师, 中国科学院植物研究所大数据与AI生物多样性保护研究中心副主任。致力于物种大数据与人工智能识别应用软件的研发。主持建设了中国植物图像库、iPlant 植物智——中国植物物种信息系统; 研发了“花伴侣”和“花伴侣专业版”等植物智能识别应用产品。拥有软件著作权登记多项。主编《植物星球》和《植物科学数据规范》等图书30余部。相关论文发表在The Innovation、Science Bulletin及Earth System Science Data等期刊。2015年被《中国科学报》评为十大科普人物, 2018年入选“中国科学院关键技术人才”, 2023年获聘中国科学院特聘研究岗位(骨干岗位)。E-mail: iplant@ibcas.ac.cn
作者简介:^†共同第一作者
基金资助:
国家重点研发计划(2021YFB3901102)

An Artificial Intelligence Model for Identifying Grassland Plants in Northern China

Jing Xuan¹^,²^,^†, Qidi Fu¹^,^†, Gan Xie¹^,²^,^†, Kai Xue¹, Hairui Luo¹^,², Ze Wei¹, Mingyue Zhao¹, Liang Zhi¹, Huawei Wan³, Jixi Gao³, Min Li¹^,²^,^*()

¹Big Data and AI Biodiversity Conservation Research Center, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²China National Botanical Garden, Beijing 100093, China
³Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China

Received:2024-02-23 Accepted:2024-06-21 Online:2025-01-10 Published:2024-06-24
Contact: * E-mail: iplant@ibcas.ac.cn
About author:^†These authors contributed equally to this paper

1. 附表.pdf(3049KB)

摘要/Abstract

摘要： 近年来, 大量植物图像智能识别相关应用软件被开发出来, 然而这些智能识别软件大多针对全国范围内的常见物种, 无法满足特定区域和特定植被类型的物种识别需求。该研究借助中国植物图像库中的图像数据集, 开发了一个人工智能模型, 用于识别内蒙古呼伦贝尔湿润草原和锡林浩特典型草原的优势和建群植物, 在实际野外测试中种级TOP5识别准确率达94.6%, 为实现特定区域植物物种智能识别提供了一种新方法。

关键词: 草地植物, 人工智能, 图像识别, 呼伦贝尔, 锡林浩特

Abstract: A large number of software applications for plant identification based on plant images have been developed in recent years. However, those applications are mostly used for identifying the common species countrywide, and thus cannot meet the needs of identifying region-specific vegetation types. In this study, we developed an artificial intelligence model for identifying the dominant plants in Hulunbeier and Xilinhot grassland in Inner Mongolia, based on the image datasets in the Plant Photo Bank of China. The Top5 accuracy of this model reaches 94.6% in the actual field identification tests. Our model provides a new method for the intelligent identification of the major plant species in a specific area.

Key words: grassland plants, artificial intelligence, image recognition, Hulunbeier, Xilinhot

宣晶, 付其迪, 谢淦, 薛凯, 雒海瑞, 魏泽, 赵明月, 智亮, 万华伟, 高吉喜, 李敏. 北方草地植物物种智能识别模型构建及应用. 植物学报, 2025, 60(1): 74-80.

Jing Xuan, Qidi Fu, Gan Xie, Kai Xue, Hairui Luo, Ze Wei, Mingyue Zhao, Liang Zhi, Huawei Wan, Jixi Gao, Min Li. An Artificial Intelligence Model for Identifying Grassland Plants in Northern China. Chinese Bulletin of Botany, 2025, 60(1): 74-80.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB24027

https://www.chinbullbotany.com/CN/Y2025/V60/I1/74

图/表 3

图1 人工智能识别训练流程图

Figure 1 Flowchart of artificial intelligence recognition training

图2 216种草地植物识别模型(A)和15K智能识别模型(B)识别野外拍摄草地植物的准确率

Figure 2 The accuracy of 216 species grassland plants intelligent recognition model (A) and 15K intelligent recognition model (B) in identifying grassland plants captured in the field

图3 草地植物智能识别手机应用程序界面

Figure 3 Cell phone application interface for intelligent recognition of grassland plants

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北方草地植物物种智能识别模型构建及应用

An Artificial Intelligence Model for Identifying Grassland Plants in Northern China

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