草地植物多样性无人机调查的物种智能识别模型构建

doi:10.17520/biods.2024236

生物多样性 ›› 2025, Vol. 33 ›› Issue (4): 24236. DOI: 10.17520/biods.2024236 cstr: 32101.14.biods.2024236

草地植物多样性无人机调查的物种智能识别模型构建

谢淦¹^,²^,^#, 宣晶¹^,²^,^#, 付其迪¹^,^#, 魏泽¹, 薛凯¹, 雒海瑞¹, 高吉喜³^,^*(), 李敏¹^,²^,^*()

1.中国科学院植物研究所, 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:2024-06-14 接受日期:2024-10-15 出版日期:2025-04-20 发布日期:2025-01-03
通讯作者: *E-mail: gjx@nies.org; 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)

Establishing an intelligent identification model for unmanned aerial vehicle surveys of grassland plant diversity

Xie Gan¹^,²^,^#, Xuan Jing¹^,²^,^#, Fu Qidi¹^,^#, Wei Ze¹, Xue Kai¹, Luo Hairui¹, Gao Jixi³^,^*(), Li Min¹^,²^,^*()

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

Received:2024-06-14 Accepted:2024-10-15 Online:2025-04-20 Published:2025-01-03
Contact: *E-mail: gjx@nies.org; iplant@ibcas.ac.cn
About author:
#Co-first authors
Supported by:
National Key Research Development Program of China(2021YFB3901102)

1. 附录.zip(57KB)

摘要/Abstract

摘要：

受全球气候变化和人类活动的影响, 植物生存环境、生存状态甚至整个植被生态系统都处于动态变化之中。对植物进行野外定点调查和监测, 是科学界和相关管理部门了解和评估区域植物生存状态、推测其发展态势, 进而制定相关保护方案等的基础。然而, 传统的植物野外调查依赖于植物鉴定专家, 这意味着极大的人力、物力和财力投入, 并且往往难以找到合适的专家。智能识别已被证明能够实现植物的种级精准鉴定, 并有极大的潜力能提高效率并减少工作量。本研究以欧亚草原东段的内蒙古草原为例, 通过在呼伦贝尔进行预实验, 发现以无人机垂直向下90°拍摄的图像用于构建模型可发现的物种数最多。基于在呼伦贝尔、锡林浩特、鄂尔多斯三地采集的无人机植物图像, 利用SSD-MobileNetV2-FPN架构训练无人机植物目标检测模型, 对采集图像进行了植物目标检测和提取, 利用MobileNetV3架构进行了识图训练, 最终搭建了可识别22科54属70种常见草地植物的无人机图像智能识别模型。利用预留的70种10,734幅图像进行评测, 该识别模型正确识别了其中的9,513幅, Top1识别准确率达到88.6%。该模型和无人机图像采集结合, 可在80 min内完成约300个1 m × 1 m样方的调查, 极大提升了植物野外调查的效率。本研究为草地植物多样性和生态系统的智能化调查和长期定点监测提供了一个新工具, 并为其他地区和植被类型中开展类似的工作提供了可参考的技术方案。

关键词: 无人机, 人工智能, 物种识别, 草地植物, 生物多样性

Abstract

Aims: Under the influence of global climate change and human activities, plant living environment, their living conditions, and even entire vegetation ecosystems are undergoing dynamic changes. Conducting field investigations and monitoring on plants are the foundation for the scientific community and relevant administrative departments to assess regional plant populations, speculate their development trends, and develop appropriate conservation strategies. However, field plant surveys have long relied on expert identification, requiring significant human, material and financial resources, and finding suitable experts is often challenging. Intelligent identification has been shown to achieve accurate, species-level identification of plants, and has great potential to improve the efficiency of field surveys while reducing the workload of experts.

Methods: In this study, we focused on the eastern part of Eurasia grasslands in Nei Mongol as a case study and employed unmanned aerial vehicle (UAVs) along with image recognition technology to develop a plant image recognition model for grasslands. Pre-experiments conducted in Hulunbeier revealed that images taken by drones at a vertical angle of 90° yielded the highest number of identifiable species for model construction. Based on the drone-captured plant images from Hulunbeier, Xilinhot, and Erdos, we used an SSD-MobileNetV2-FPN architecture to train a drone-based plant object detection model. This model detected and extracted plant detection model. Further image recognition training using the MobileNetV3 architecture allowed us to construct an intelligent recognition model capable of identifying 70 common grassland plant species belonging to 54 genera across 22 families.

Results:Using the reserved 10,734 images of 70 species for evaluation, the model correctly identified 9,513 images, achieving a Top1 recognition accuracy of 88.6%. When combined with UAV-based image acquisition, this model can survey approximately 300 1 m × 1 m quadrats in 80 min, significantly improving the efficiency of plant field investigation.

Conclusion: This study provides a new tool for intelligent surveys and long-term site-based monitoring of grassland plant diversity and ecosystems. It reduces the dependence on experts for plant identification, providing a practical tool for biodiversity and ecosystem monitoring at the grassroots level. Additionally, this model offers a template for similar efforts in other regions and vegetation types.

Key words: unmanned aerial vehicle (UAV), artificial intelligence (AI), species recognition, grassland plants, biodiversity

谢淦, 宣晶, 付其迪, 魏泽, 薛凯, 雒海瑞, 高吉喜, 李敏 (2025) 草地植物多样性无人机调查的物种智能识别模型构建. 生物多样性, 33, 24236. DOI: 10.17520/biods.2024236.

Xie Gan, Xuan Jing, Fu Qidi, Wei Ze, Xue Kai, Luo Hairui, Gao Jixi, Li Min (2025) Establishing an intelligent identification model for unmanned aerial vehicle surveys of grassland plant diversity. Biodiversity Science, 33, 24236. DOI: 10.17520/biods.2024236.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024236

https://www.biodiversity-science.net/CN/Y2025/V33/I4/24236

图/表 6

图1 无人机草地植物图像智能识别实现流程图

Fig. 1 Process for intelligent identification of grassland plant images captured by unmanned aerial vehicle (UAV)

图2 无人机拍摄图像目标检测人工框选标注示例

Fig. 2 Manual labeling for target detection in images captured by unmanned aerial vehicle (UAV)

图3 目标检测模型智能框选拍摄样线照片情况

Fig. 3 Intelligent frame selection of target detection model for taking line transect photos

图4 用于构建识别模型的无人机植物图像数据集(左: 物种数据集; 右: 物种图像示例)

Fig. 4 Image dataset for establishing identification models (Left, Dataset of species; Right, Images of species)

图5 无人机植物图像智能识别模型的识别准确率

Fig. 5 Identification accuracy of intelligent models for plant images taken by unmanned aerial vehicle (UAV)

图6 草地植物多样性无人机调查的物种智能识别系统网站平台(http://www.iplant.cn/tc)

Fig. 6 Platform of Intelligent Species Identification System Website for investigation of grassland plant diversity by unmanned aerial vehicle (UAV) (http://www.iplant.cn/tc)

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草地植物多样性无人机调查的物种智能识别模型构建

Establishing an intelligent identification model for unmanned aerial vehicle surveys of grassland plant diversity

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