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 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.
Highlights
Plant cultivar identification is a fundamental field of interest for plant breeding in terms of cultivar rights protection and the continuous breeding of new cultivars
We propose a leaf image-based and automatic plant cultivar classification pipeline, called MFCIS (Multi-feature Combined Cultivar Identification System), that combines morphological features of the leaf shape, texture, and venation extracted by persistent homology (PH) and the high-level image features extracted by the fine-tuned Xception[32] model
Owing to the high resolution of leaf images, the texture and venation persistence diagram25 (PD) contained a large number of points
Summary
Plant cultivar identification is a fundamental field of interest for plant breeding in terms of cultivar rights protection and the continuous breeding of new cultivars. We evaluated the accuracies of the cultivar identification models with leaf features extracted by some image processing-based methods to exhibit the advantages of high-level image features (Table 1). Their parameter settings are presented in supplementary materials section 2. The classification model using only leaf morphological features extracted by PH (referred to as the PH method in Tables 1 and 2) was tested and obtained an accuracy of 42.08% Even though it achieved much higher performance than IDSC + DP and HSC, it was less accurate than both the MFCIS pipeline and the fine-tuned Xception pipeline
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