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

ABSTRACT: 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

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8325680 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature