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)

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)

参考文献 28

[1]	Cao JJ, Leng WC, Liu K, Liu L, He Z, Zhu YH (2018) Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing, 10, 89.
[2]	Chiu YC, Tsai CY, Ruan MD, Shen GY, Lee TT (2020) Mobilenet-SSDv2:An improved object detection model for embedded systems. In: 2020 International Conference on System Science and Engineering, pp. 1-5. Kagawa, Japan.
[3]	Du C, Liu J, Liu S, Ma JS (2022) A current and historical situation report of Chinese plant taxonomists. Biodiversity Science, 30, 22355. (in Chinese with English abstract)
	[杜诚, 刘军, 刘夙, 马金双 (2022) 中国植物分类学者的历史与现状. 生物多样性, 30, 22355.]
[4]	Emin M, Anwar E, Liu SH, Emin B, Mamut M, Abdukeram A, Liu T (2021) Target detection-based tree recognition in a spruce forest area with a high tree density—Implications for estimating tree numbers. Sustainability, 13, 3279.
[5]	Hong QQ, Zhong XY, Chen WT, Zhang ZH, Li B (2023) Hyperspectral image classification network based on 3D octave convolution and multiscale depthwise separable convolution. ISPRS International Journal of Geo-Information, 12, 505.
[6]	Husson E, Hagner O, Ecke F (2014) Unmanned aircraft systems help to map aquatic vegetation. Applied Vegetation Science, 17, 567-577.
[7]	LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature, 521, 436-444.
[8]	Leduc MB, Knudby AJ (2018) Mapping wild leek through the forest canopy using a UAV. Remote Sensing, 10, 70.
[9]	Li AL, Dai ZG, Chen JQ, Deng CH, Tang Q, Cheng CH, Xu Y, Zhang XY, Su JG, Yang ZM (2023) Progresses of machine learning application in plant phenotype research. Plant Fiber Sciences in China, 45(5), 248-253, 260. (in Chinese with English abstract)
	[李阿蕾, 戴志刚, 陈基权, 邓灿辉, 唐蜻, 程超华, 许英, 张小雨, 粟建光, 杨泽茂 (2023) 机器学习在植物表型中的应用进展. 中国麻业科学, 45(5), 248-253, 260.]
[10]	Lin TY, Dollár P, Girshick R, He KM, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125. Honolulu, HI, USA.
[11]	Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD:Single shot multibox detector. In:Computer Vision—ECCV 2016: 14th European Conference, pp. 21-37. Amsterdam, The Netherlands.
[12]	Liu ZL, Wang J, Tian Y, Dai SL (2019) Deep learning for image-based large-flowered Chrysanthemum cultivar recognition. Plant Methods, 15, 146.
[13]	Lü LY, Zhao Y, Yu GQ (2016) History, current situation and trends of the investigation for grassland plant resources at home and abroad. Heilongjiang Animal Science and Veterinary Medicine, (19), 140-143, 150. (in Chinese with English abstract)
	[吕林有, 赵艳, 于国庆 (2023) 国内外草地植物资源调查历史现状与趋势. 黑龙江畜牧兽医, (19), 140-143, 150.]
[14]	Peng Y, Tao ZY, Xu ZY, Bai L (2020) Detection of plant species beta-diversity in Hunshandak Sandy grasslands using hyperspectral data. Spectroscopy and Spectral Analysis, 40, 2016-2022. (in Chinese with English abstract)
	[彭羽, 陶子叶, 许子妍, 白岚 (2020) 应用高光谱数据估算植物物种beta多样性. 光谱学与光谱分析, 40, 2016-2022.]
[15]	Shang SB, Fan L, Liu N, Zhang FQ (2024) Flora of seed plants in Qinghai Area of the Kunlun Mountain National Park. Acta Botanica Boreali-Occidentalia Sinica, 44, 491-501. (in Chinese with English abstract)
	[尚帅斌, 范琳, 刘楠, 张发起 (2024) 昆仑山国家公园青海片区评估区种子植物区系研究. 西北植物学报, 44, 491-501.]
[16]	Sun Y, Liu Y, Wang G, Zhang HY (2017) Deep learning for plant identification in natural environment. Computational Intelligence and Neuroscience, 2017, 7361042.
[17]	Sun Y, Yuan YX, Luo YF, Ji WX, Bian QY, Zhu ZQ, Wang JR, Qin Y, He XZ, Li M, Yi SH (2022) An improved method for monitoring multiscale plant species diversity of alpine grassland using UAV: A case study in the source region of the Yellow River, China. Frontiers in Plant Science, 13, 905715.
[18]	Sun ZY, Wang XN, Wang ZH, Yang L, Xie YC, Huang YH (2021) UAVs as remote sensing platforms in plant ecology: Review of applications and challenges. Journal of Plant Ecology, 14, 1003-1023.
[19]	Szegedy C, Loffe S, Vanhoucke V, Alemi A (2016a) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI’17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (eds Singh S, Markovitch S), pp. 4278-4284. AAAI Press, San Francisco, USA.
[20]	Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016b) Rethinking the inception architecture for computer vision. In: Conference on Computer Vision and Pattern Recognition, pp. 2818-2826. Las Vegas, NV, USA.
[21]	Wan FM, Wan HW, Zhang ZR, Gao JX, Sun CX, Wang YC (2024) The application potential of unmanned aerial vehicle surveys in grassland plant diversity. Biodiversity Science, 32, 23381. (in Chinese with English abstract)
	[万凤鸣, 万华伟, 张志如, 高吉喜, 孙晨曦, 王永财 (2024) 草地植物多样性无人机调查的应用潜力. 生物多样性, 32, 23381.]
[22]	Wang HM, Liu H, Sang J, Li XY, Su M (2020) Spatial-temporal characteristics of vegetation cover and the correlation with climate in Hulunbuir. Journal of Inner Mongolia University (Natural Science Edition), 51, 539-547. (in Chinese with English abstract)
	[王海梅, 刘昊, 桑婧, 李鑫杨, 苏明 (2020) 呼伦贝尔植被覆盖时空变化特征及其与气候的相关性分析. 内蒙古大学学报(自然科学版), 51, 539-547.]
[23]	Wang YP, Cao SS, Li QS, Sun W (2023) Lightweight classification and recognition method of natural grassland plant image under complex background based on structure reparameterization. Journal of West China Forestry Science, 52(4), 144-153. (in Chinese with English abstract)
	[王亚鹏, 曹姗姗, 李全胜, 孙伟 (2023) 基于结构重参数化的复杂背景下天然草地植物图像轻量级分类识别方法. 西部林业科学, 52(4), 144-153.]
[24]	Xie G, Xuan J, Liu B, Luo YB, Wang YF, Zou XH, Li M (2024) FlowerMate 2.0: Identifying plants in China with artificial intelligence. The Innovation, 5, 100636.
[25]	Xu ZH, Liu SY, Zhao Y, Tu WQ, Chang ZF, Zhang ET, Guo J, Zheng D, Geng J, Gu GY, Guo CP, Guo LL, Wang J, Xu CY, Peng C, Yang T, Cui MQ, Sun WC, Zhang JT, Liu HT, Ba CQ, Wang HQ, Jia JC, Wu JZ, Xiao C, Ma KP (2020) Evaluation of the identification ability of eight commonly used plant identification application softwares in China. Biodiversity Science, 28, 524-533. (in Chinese with English abstract)
	[许展慧, 刘诗尧, 赵莹, 涂文琴, 常诏峰, 张恩涛, 郭靖, 郑迪, 耿鋆, 顾高营, 郭淳鹏, 郭璐璐, 王静, 徐春阳, 彭钏, 杨腾, 崔梦琪, 孙伟成, 张剑坛, 刘皓天, 巴超群, 王鹤琪, 贾竞超, 武金洲, 肖翠, 马克平 (2020) 国内8款常用植物识别软件的识别能力评价. 生物多样性, 28, 524-533.]
[26]	Zhang LX, Yue X, Zhou DC, Fan JW, Li YZ (2023) Impacts of climate change and human activities on vegetation restoration in typical grasslands of China. Environmental Science, 44, 2694-2703. (in Chinese with English abstract)
	[张良侠, 岳笑, 周德成, 樊江文, 李愈哲 (2023) 气候变化和人类活动对我国典型草原区植被恢复的影响. 环境科学, 44, 2694-2703.]
[27]	Zhang XS (2007) Vegetation Map of the People’s Republic of China (1 : 1,000,000). Geology Press, Beijing. (in Chinese)
	[张新时 (2007) 中华人民共和国植被图(1 : 1,000,000). 地质出版社, 北京.]
[28]	Zhao P, Guo YX, Duan D, Liu WZ (2018) The application of plant identifications apps with mobile phone in botany teaching. Biology Teaching in University (Electronic Edition), 8(1), 47-51. (in Chinese with English abstract)
	[赵鹏, 郭垚鑫, 段栋, 刘文哲 (2018) 智能手机植物识别App在植物学教学中的应用. 高校生物学教学研究(电子版), 8(1), 47-51.]

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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