您好，欢迎访问山东省农业科学院文献资源数据库平台

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

文献类型： 外文期刊

作者： Zhang, Yanping 1 ; Peng, Jing 1 ; Yuan, Xiaohui 1 ; Zhang, Lisi 3 ; Zhu, Dongzi 3 ; Hong, Po 3 ; Wang, Jiawei 3 ; Liu, Qi 1 ;

作者机构： 1.Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan, Hubei, Peoples R China

2.Wuhan Univ Technol, Chongqing Res Inst, Chongqing, Peoples R China

3.Shandong Inst Pomol, Shandong Key Lab Fruit Biotechnol Breeding, Tai An, Shandong, Peoples R China

期刊名称：HORTICULTURE RESEARCH （ 影响因子：6.793； 五年影响因子：6.589 ）

ISSN： 2662-6810

年卷期： 2021 年 8 卷 1 期

页码：

收录情况： SCI

摘要： Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：山东省农业科学院文献资源数据库平台

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台