A GIS-based method for the analysis of digital rhizotron images

IF 1 Q3 PLANT SCIENCES

Plant Root Pub Date : 2011-01-01 DOI:10.3117/PLANTROOT.5.69

C. Gasch, T. Collier, S. Enloe, S. Prager

{"title":"A GIS-based method for the analysis of digital rhizotron images","authors":"C. Gasch, T. Collier, S. Enloe, S. Prager","doi":"10.3117/PLANTROOT.5.69","DOIUrl":null,"url":null,"abstract":"Quantification of belowground plant response via rhizotron root image analysis is difficult and time-consuming, yet a plant's root response is of great interest to many researchers. Here, we present an automated, time efficient method for examining digital rhizotron images. A total of 285 digital images (218 mm by 300 mm) were collected using a flatbed scanner from 16 rhizotron boxes from an experiment designed to evaluate the root response of Dalmatian toadflax, Linaria dalmatica (L.) Miller to herbivory by the Dalmatian toadflax stem mining weevil, Mecinus janthinus Germar, a widely used biological control agent. Images were quantified for root length and area using two methods: manually digitizing images using Root Measurement System (RMS) software, and semi- automated analysis using Feature Analyst™, an extension for a geographic information system. Feature Analyst length and area values were highly positively correlated with RMS area values, but were not correlated with RMS length measurements. The semi-automated Feature Analyst approach required one-eighth of the time required to analyze images using the manual RMS method. Feature Analyst for digital image analysis warrants more investigation, but appears to be a promising method for quantifying belowground plant characteristics.","PeriodicalId":20205,"journal":{"name":"Plant Root","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3117/PLANTROOT.5.69","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Root","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3117/PLANTROOT.5.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PLANT SCIENCES","Score":null,"Total":0}

引用次数: 7

Abstract

Quantification of belowground plant response via rhizotron root image analysis is difficult and time-consuming, yet a plant's root response is of great interest to many researchers. Here, we present an automated, time efficient method for examining digital rhizotron images. A total of 285 digital images (218 mm by 300 mm) were collected using a flatbed scanner from 16 rhizotron boxes from an experiment designed to evaluate the root response of Dalmatian toadflax, Linaria dalmatica (L.) Miller to herbivory by the Dalmatian toadflax stem mining weevil, Mecinus janthinus Germar, a widely used biological control agent. Images were quantified for root length and area using two methods: manually digitizing images using Root Measurement System (RMS) software, and semi- automated analysis using Feature Analyst™, an extension for a geographic information system. Feature Analyst length and area values were highly positively correlated with RMS area values, but were not correlated with RMS length measurements. The semi-automated Feature Analyst approach required one-eighth of the time required to analyze images using the manual RMS method. Feature Analyst for digital image analysis warrants more investigation, but appears to be a promising method for quantifying belowground plant characteristics.

查看原文本刊更多论文

一种基于gis的数字根瘤苗图像分析方法

通过根际根系图像分析来量化地下植物的响应是困难且耗时的，但植物的根系响应是许多研究人员非常感兴趣的。在这里，我们提出了一个自动化的，时间效率的方法来检查数字根状芽图像。利用平板扫描仪从16个根管箱中采集了285张(218 mm × 300 mm)的数字图像，用于评估达尔马提亚蟾蜍(Linaria dalmatica, L.)根系的响应。米勒以草食达尔马提亚蟾蜍茎采象甲，是一种广泛使用的生物防治剂。使用两种方法对图像进行根长度和面积的量化:使用根测量系统(RMS)软件对图像进行手动数字化，以及使用Feature Analyst™(地理信息系统的扩展)进行半自动分析。特征分析长度和面积值与RMS面积值高度正相关，但与RMS长度测量值不相关。半自动Feature Analyst方法所需的时间是使用手动RMS方法分析图像所需时间的八分之一。用于数字图像分析的特征分析需要更多的研究，但似乎是一种很有前途的量化地下植物特征的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Plant Root PLANT SCIENCES-

CiteScore

1.50

自引率

0.00%

发文量

期刊介绍： Plant Root publishes original papers, either theoretical or experimental, that provide novel insights into plant roots. The Journal’s subjects include, but are not restricted to, anatomy and morphology, cellular and molecular biology, biochemistry, physiology, interactions with soil, mineral nutrients, water, symbionts and pathogens, food culture, together with ecological, genetic and methodological aspects related to plant roots and rhizosphere. Work at any scale, from the molecular to the community level, is welcomed.

文献相关原料

公司名称	产品信息	采购帮参考价格