Construction of intelligent identification model and application for grassland plants in northern China #br#

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  • 1State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; 2Big Data and AI Research Center of Biodiversity Conservation, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; 3China National Botanical Garden, Beijing 100093, China; 4Satellite Application Center for Ecology and Environment, MEE, Beijing 100094, China

Received date: 2024-02-23

  Revised date: 2024-05-24

  Online published: 2024-06-24

Abstract

A large number of applications on plant intelligent identification have been developed for use by researchers, managers and the public in recent years, and image identification has gradually moved from theory to practice. However, most of those applications is for the identification of widespread species or common species in the whole country, which is difficult to meet the needs of plant identification in specific regions and vegetation types. In this study, we have completed an identification model of dominant plants in Hulunbeier and Xilinhot grassland in Inner Mongolia using image dataset from Plant Photo Bank of China. The TOP1 accuracy of this model reaches more than 85% in the actual field identification activities, which provides a referable case for the realization of intelligent identification of plant species in a specific area.


Cite this article

Xuan Jing, Fu Qidi, Xie Gan, Xue Kai, Luo Hairui, Wei Ze, Zhao Mingyue, Zhi Liang, Wan Huawei, Gao Jixi, Li Min . Construction of intelligent identification model and application for grassland plants in northern China #br#[J]. Chinese Bulletin of Botany, 0 : 0 -0 . DOI: 10.11983/CBB24027

References

李敏 (2018). “花伴侣”: 人工智能时代知识服务的新媒介. 出版参考 (08): 23–24.
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.
杨旭光 (2022). 基于卷积神经网络模型水生植物分类识别研究与系统研发. 山东农业大学.
张勇辉, 冯琦胜, 梁天刚, 高新华, 黄晓东, 孙德伟, 吴安东 (2023). 草地资源调查与智能分析系统简介. 草业科学 40(08), 2171–2178.
LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature 521, 436–444.
Liu Z, Wang J, Tian Y, Dai S (2019). Deep learning for image-based large-flowered chrysanthemum cultivar recognition. Plant Methods 15, 146. https://doi.org/10.1186/s13007-019-0532-7.
Joly A., Go?au H., Botella C., Glotin H, Bonnet P, Vellinga W-P, Planqué R, Müller H (2018). Overview of LifeCLEF 2018: A Large-Scale Evaluation of Species Identification and Recommendation Algorithms in the Era of AI. In: Bellot P, Trabelsi C, Mothe J, Murtagh F, Nie JY, Soulier L, SanJuan E, Cappellato L, Ferro N (Eds.) CLEF 2018: Experimental IR Meets Multilinguality, Multimodality, and Interaction. Lecture Notes in Computer Science, 11018, 247–266. https: //doi.org/10.1007/978-3-319-98932-7_24
Szegedy C, Loffe S, Vanhoucke V, Alemi A (2016a). Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Singh S, Markovitch S (Eds.). AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. AAAI Press, 4278–4284.
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 (CVPR). 2818–2826.
Grinblat L G, Uzal C L, Larese G M, Granitto P M (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127, 418–424.
Ledford H (2017). Artificial intelligence identifies plant species for science. Nature. https://doi.org/10.1038/nature.2017.22442
Marco S, Michael R, David B, W?ldchen J, M?der P (2019). Image-based classification of plant genus and family for trained and untrained plant species. BMC bioinformatics 20: 4.
Nguyen T H, Nguyen T L, Sidorov D N, Dreglea A I (2017). Machine learning algorithms application to road defects classification. Intelligent Decision Technologies 12(1): 59–66.
Cerutti G, Tougne L, Mille J, Vacavant A, Coquin D (2013). Understanding leaves in natural images – A model-based approach for tree species identification. Computer Vision and Image Understanding 117(10): 1482–1501.
Kumar N, Belhumeur P N, Biswas A, Jacobs D W, Kress W J, Lopez I C, Soares J V B (2012). Leafsnap: A computer vision system for automatic plant species identification. In: Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (Eds.) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science 7573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33709-3_36
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