Abstract
Plant classification and recognition research is the basic research work of botany research and agricultural production. It is of great significance to identify and distinguish plant species and explore the relationship between plants. In recent years, most of the research methods focus on feature extraction and feature engineering related aspects. In this paper, a plant leaf recognition method based on fine-grained image classification is proposed, which can better find the regional block information of different species of plant leaves. In this study, the hierarchical and progressive training strategy is adopted, the method of cutting and generating jigsaw is used to force the model to find information of different granularity levels. The experiment proves that the model trained by the fine-grained classification method can better solve the problems of large intra-class spacing and small inter-class spacing of plant slices.
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Acknowledgements
This work was supported by the grant of National Key R&D Program of China, No. 2018AAA0100100; in part by supported by National Natural Science Foundation of China, Nos. 61861146002, 61772370, 61732012, 61932008, 61772357, 62073231, and 62002266; in part by the Scientific & Technological Base and Talent Special Program of the Guangxi Zhuang Autonomous Region, GuiKe AD18126015; and in part by “BAGUI Scholar” Program of Guangxi Province of China.
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Liu, W., Yuan, C., Qin, X., Wu, H. (2021). Plant Leaf Recognition Network Based on Fine-Grained Visual Classification. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_43
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