Peige Zhong , Xiaojun Liu , Yulu Ye , Rui Zhang , Hu Zhou , Yan Guo , Baoguo Li , Jinyu Zhu , Yuntao Ma
{"title":"An automatic landmarking algorithm for leaf morphology based on conformal mapping","authors":"Peige Zhong , Xiaojun Liu , Yulu Ye , Rui Zhang , Hu Zhou , Yan Guo , Baoguo Li , Jinyu Zhu , Yuntao Ma","doi":"10.1016/j.compag.2025.110274","DOIUrl":null,"url":null,"abstract":"<div><div>Leaf shape is of great significance in plant phenotype research. Landmarks method is a widely used morphometric approach, which can comprehensively describe the morphological differences among leaves. However, the selection of landmarks is time-consuming and laborious. An automatic landmarking algorithm is proposed here. Based on conformal mapping, the leaf outline can be transformed into a monotonically increasing function curve, referred to as the ’fingerprint function’. The Dynamic Time Warping (DTW) algorithm was introduced to match landmarks between different leaves. Two leaf datasets were used to validate the algorithm separately in different species and developmental stages. Dataset1 is a public dataset which covers 26 different types of leaves. The average positional difference between automatic and manual landmarks for dataset1 was only 2.95%. Dataset2 consists of cotton leaves collected in the field at various growth stages, and the positional difference for this dataset was all below 5%. These results validate that our algorithm is applicable to a wide range of leaf types and capable of identifying and locating novel features that emerge during leaf growth. The automatic landmarking algorithm can simulate manual landmarking to a great extent. It provides a new approach for automated acquisition of plant leaf shape homology tailored to the research needs of botanists.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110274"},"PeriodicalIF":8.9000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925003801","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Leaf shape is of great significance in plant phenotype research. Landmarks method is a widely used morphometric approach, which can comprehensively describe the morphological differences among leaves. However, the selection of landmarks is time-consuming and laborious. An automatic landmarking algorithm is proposed here. Based on conformal mapping, the leaf outline can be transformed into a monotonically increasing function curve, referred to as the ’fingerprint function’. The Dynamic Time Warping (DTW) algorithm was introduced to match landmarks between different leaves. Two leaf datasets were used to validate the algorithm separately in different species and developmental stages. Dataset1 is a public dataset which covers 26 different types of leaves. The average positional difference between automatic and manual landmarks for dataset1 was only 2.95%. Dataset2 consists of cotton leaves collected in the field at various growth stages, and the positional difference for this dataset was all below 5%. These results validate that our algorithm is applicable to a wide range of leaf types and capable of identifying and locating novel features that emerge during leaf growth. The automatic landmarking algorithm can simulate manual landmarking to a great extent. It provides a new approach for automated acquisition of plant leaf shape homology tailored to the research needs of botanists.
叶片形状在植物表型研究中具有重要意义。标志法是一种广泛应用的形态计量学方法,它能全面地描述叶片之间的形态差异。然而,选择地标是费时费力的。本文提出了一种自动地标算法。在保角映射的基础上,将叶片轮廓变换成一条单调递增的函数曲线,称为“指纹函数”。引入动态时间翘曲(Dynamic Time Warping, DTW)算法来匹配不同叶片之间的地标。利用两个不同物种和发育阶段的叶片数据分别对算法进行验证。Dataset1是一个公共数据集,包含26种不同类型的叶子。在dataset1中,自动地标与手动地标的平均位置差仅为2.95%。Dataset2由不同生育期田间采集的棉花叶片组成,该数据集的位置差均在5%以下。这些结果验证了我们的算法适用于广泛的叶片类型,能够识别和定位叶片生长过程中出现的新特征。自动地标算法可以在很大程度上模拟人工地标。它提供了一种适合植物学家研究需要的植物叶片形状同源性自动获取的新方法。
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.