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

• Published in: Horticulture research Vol. 8; no. 1
• Main Authors: Zhang, Yanping, Peng, Jing, Yuan, Xiaohui, Zhang, Lisi, Zhu, Dongzi, Hong, Po, Wang, Jiawei, Liu, Qingzhong, Liu, Weizhen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.08.2021; Oxford University Press
• Subjects: 631/158/670; 631/1647/48; 631/449/711; 631/61/447/2312; Accuracy; Agriculture; Algorithms; Artificial neural networks; Biomedical and Life Sciences; Cultivars; Datasets; Deep learning; Ecology; Feature extraction; Fruits; Germplasm; Glycine max; Homology; Identification; Leaves; Life Sciences; Machine learning; Neural networks; Pipelines; Plant breeding; Plant Breeding/Biotechnology; Plant Genetics and Genomics; Plant Sciences; Plant species; Plants; Property rights; Prunus avium; Soybeans; Species; Venation; Web services

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 identifi...