中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network

文献类型:期刊论文

作者Zeng,Mingzhao1,2; Gao,Huiyi1; Wan,Li1
刊名Journal of Physics: Conference Series
出版日期2021-04-01
卷号1883
ISSN号1742-6588
DOI10.1088/1742-6596/1883/1/012093
英文摘要Abstract The treatment and prevention of crop diseases have an extremely important impact on the yield and quality of crops. In recent years, with the development of computer vision and deep learning technology, research on crop disease recognition based on leaf images has received extensive attention. In the field of grape disease recognition, the lack of large-scale diseased leaf labeling data sets limits the accuracy of recognition, and obtaining professional grape disease data sets requires a lot of manpower and material resources. Aiming at the problem of the lack of grape leaf data set, this research proposes a data generation model based on the cycle Generative Adversarial Network model which introduced an leaf foreground module (LFM) block. Experiments show that the model can generate high-quality grape leaf disease images, which can improve the accuracy of grape disease recognition task in a Few-Shot Grape Leaf Diseases Classification task.
语种英语
WOS记录号IOP:1742-6588-1883-1-012093
出版者IOP Publishing
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/121875]  
专题中国科学院合肥物质科学研究院
作者单位1.Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei 230031, China
2.University of Science and Technology of China, Hefei 230026, China
推荐引用方式
GB/T 7714
Zeng,Mingzhao,Gao,Huiyi,Wan,Li. Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network[J]. Journal of Physics: Conference Series,2021,1883.
APA Zeng,Mingzhao,Gao,Huiyi,&Wan,Li.(2021).Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network.Journal of Physics: Conference Series,1883.
MLA Zeng,Mingzhao,et al."Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network".Journal of Physics: Conference Series 1883(2021).

入库方式: OAI收割

来源:合肥物质科学研究院

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