植物分类学的学习与研究:系统学综述

W. Kusumawardani, Muzzazinah, M. Ramli
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引用次数: 3

摘要

植物鉴定分类概念是生物专业学生必须掌握的重要基础知识。本研究旨在了解植物分类学学习的概念和方法,以及植物分类学研究的对象和方法。选取2005-2019年发表的17篇文献作为综述材料。9篇是关于植物分类学的学习,8篇是关于植物分类学的研究。文章来源于《生物教育杂志》和《科学指南》。结果显示了在植物鉴定和分类中所学到的常见概念。在学习中使用的主要植物类群是:苔藓植物、蕨类植物、裸子植物和被子植物,以本地种和局灶种为例。学习方法和途径多种多样,包括:利用真实植物标本、二分类键法、基于助记法的单词联想练习,以及识别本地植物的图形纸牌游戏。还有人使用电子多通道键,iPod上的iOS应用程序进行植物识别指导,交互式多媒体植物识别二分键,标记绘制和本地植物识别描述性文字。植物分类学的研究对象是植物的各个方面,其中之一就是叶片。植物分类学研究中使用的各种方法,如:FRT、LDC Linear、kNN、SIFT、Color矩、SFTA、ann、深度学习技术、分层方法- NFC和AIT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plant taxonomy learning and research: A systematics review
Concepts of plant identification and classification were important basic knowledge to be mastered by biology students. The research was aimed to find out what concepts and methods of learning plant taxonomy, and find out the objects and methods in plant taxonomy research. Seventeen articles published from 2005-2019 were selected as the review materials. Nine articles were about learning the plant taxonomy, and eight articles were about research on plant taxonomy. The articles were obtained from Journal of Biological Education and Science Direct. The results showed the common concepts learned about plant identification and classification. The prominent plant groups used in the learning were: the Bryophytes, Pteridophytes, Gymnosperms, and Angiosperm with the example of the native species and focal species. The learning methods and approaches were varied, including: using real plant specimens, dichotomous key method, word association exercise based on mnemonics approach, or pictorial card games for identification native plants. Others use an electronic multi-access key, iOS app on the iPod for plant identification guide, interactive multimedia dichotomous key for plant identification, labeled drawing and descriptive writing of native plant identification. Various aspects used as the object of the research on plants taxonomy, one of them was the leaves. Various methods used in the research on plant taxonomy, such as: FRT, LDC Linear, kNN, SIFT, Color moments, SFTA, ANNs, Deep learning techniques, hierarchical approach - NFC, and AIT.
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