Plant leaf tooth feature extraction.

Abstract

Leaf tooth can indicate several systematically informative features and is extremely useful for circumscribing fossil leaf taxa. Moreover, it can help discriminate species or even higher taxa accurately. Previous studies extract features that are not strictly defined in botany; therefore, a uniform standard to compare the accuracies of various feature extraction methods cannot be used. For efficient and automatic retrieval of plant leaves from a leaf database, in this study, we propose an image-based description and measurement of leaf teeth by referring to the leaf structure classification system in botany. First, image preprocessing is carried out to obtain a binary map of plant leaves. Then, corner detection based on the curvature scale-space (CSS) algorithm is used to extract the inflection point from the edges; next, the leaf tooth apex is extracted by screening the convex points; then, according to the definition of the leaf structure, the characteristics of the leaf teeth are described and measured in terms of number of orders of teeth, tooth spacing, number of teeth, sinus shape, and tooth shape. In this manner, data extracted from the algorithm can not only be used to classify plants, but also provide scientific and standardized data to understand the history of plant evolution. Finally, to verify the effectiveness of the extraction method, we used simple linear discriminant analysis and multiclass support vector machine to classify leaves. The results show that the proposed method achieves high accuracy that is superior to that of other methods.

Plant phenotyping relevance

葉の画像から葉縁の歯状形態を自動抽出・測定する手法を開発し、分類精度で有効性を検証しており、植物表現型取得が研究の中心である。

Code and data availability

The paper's Data Availability statement deposits the minimal dataset (leaf tooth feature data underlying the measurements) on Figshare at a public link. No author analysis code or trained models are explicitly deposited; the Swedish Leaf and Flavia datasets are cited prior public resources, not paper-specific assets.