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.

中文翻译：

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MFCIS：使用深度学习和持久同源性的植物品种自动基于叶片的识别管道

可靠、高效地识别植物品种有利于植物育种者的产权保护和种质资源创新。虽然基于叶片图像的方法已广泛应用于植物物种识别，但由于品种间叶片的高度相似性，它们很少应用于品种识别。在这里，我们提出了一种基于叶子图像的自动品种识别流程，称为 MFCIS (米超F饮食结合C多品种一世识别小号ystem），它结合了通过持久同源性和卷积神经网络（CNN）收集的多个叶子形态特征。持久同源性是一种多尺度且稳健的方法，用于提取叶片形状、纹理和脉络细节的拓扑特征。基于 CNN 的算法 Xception 网络经过微调以提取高级叶图像特征。对于水果种类，我们将 MFCIS 管道以甜樱桃为基准（鸟李L.) 叶子数据集，包含来自 88 个品种或未发布选择的 >5000 张叶子图像，平均准确率达到 83.52%。对于一年生作物物种，我们将 MFCIS 管道应用于大豆 (Glycine max L. Merr.) 叶子数据集，其中包含在五个生长期收集的 100 个栽培品种或优良育种系的 5000 张叶子图像。每个生长期的识别模型都是独立训练的，它们的结果使用分数级融合策略进行组合。分数级融合后的分类准确率为91.4%，远高于单独使用每个生长期或混合所有生长期时的准确率。为了便于采用提议的管道，我们构建了一个用户友好的 Web 服务，该服务可在以下位置免费获得http://www.mfcis.online.