Phenotyping Common Bean (Phaseolus vulgaris L.) Tolerance to Fomesafen and Imazamox Using Visible RGB Imaging Integrated With Multivariate Analysis

IF 4.4 Q1 Agricultural and Biological Sciences
Legume Science Pub Date : 2026-07-13 DOI:10.1002/leg3.70098
Carlos Zacarias Joaquim Jr., Luan Tiago dos Santos Carbonari, Carina Lopes Djadjo, Charlene Barboza Bussolaro, Mauro Bitencourt de Souza, Vagner Muniz de Oliveira, Maria Fernanda Campos Rodrigues, Fernanda Cristina Schutz Gislon, Samara Campos do Nascimento, Aires da Costa, Jorge Luis Tejada, Paulo Henrique Cerutti, Altamir Frederico Guidolin, Jefferson Luís Meirelles Coimbra
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引用次数: 0

Abstract

The common bean is vital for food security, but its productivity is often limited by competition with weeds, requiring the use of herbicides. The response of genotypes to herbicides such as fomesafen and imazamox is variable, and the traditional evaluation of phytotoxicity through visual methods is subjective. Therefore, the present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide-induced phytotoxicity in common bean genotypes, aiming to reduce the subjectivity of traditional visual assessments; (ii) characterize common bean genotypes under the effects of different herbicides and their doses in progenies and parental lines, based on morphophysiological traits and indices derived from visible RGB (red, green, and blue) digital images. The experiment was conducted in a completely randomized design under a 3 × 3 × 6 factorial scheme (herbicide × dose × genotype) with three replications, evaluating fomesafen and imazamox at doses of 0%, 100%, and 200% of the recommended rates. Data were collected on visual phytotoxicity, plant height, stem diameter, number of leaves, and image indices (Green Index, Excess Green Index, Excess Red Index, and Color Index of Vegetation Extraction). Results indicated that the triple interaction was significant, revealing the complexity of plant responses to herbicides. Canonical discriminant analysis explained 78.66% of the total variation, with the first canonical discriminant function (29.42%) contrasting structural development and vitality with stress, the second canonical discriminant function (27.59%) reflecting overall plant vigor, and the third canonical discriminant function (21.65%) capturing stress and phytotoxicity negatively affecting growth. The analysis demonstrated that image-based indices combined with multivariate techniques are effective for quantifying phytotoxicity and distinguishing genotypes (tolerant and sensitive to herbicide effects), overcoming the limitations of visual evaluations, and should be used as a complementary tool to traditional techniques. Therefore, the parental genotype IPR Campos Gerais and the progeny F1A were tolerant to herbicides at different doses, while the parental genotype BAF36 and the progeny F2B were sensitive. Hence, the proposed methodology is effective for identifying herbicide-tolerant and sensitive genotypes.

Abstract Image

菜豆(Phaseolus vulgaris L.)表型分析结合多变量分析的可见RGB成像对氟美沙芬和依莫唑的耐受性
普通豆对粮食安全至关重要,但它的产量往往受到杂草竞争的限制,需要使用除草剂。不同基因型植物对除草剂的反应是不同的,传统的目测方法对植物毒性的评价是主观的。因此,本研究的目的是:(i)提出一种基于图像分析的方法来对普通豆类基因型除草剂诱导的植物毒性进行表型分析,旨在减少传统视觉评估的主观性;(ii)根据形态生理特征和来自可见RGB(红、绿、蓝)数字图像的指数,表征不同除草剂及其剂量对子代和亲本系影响下的常见豆类基因型。试验采用完全随机设计,按3 × 3 × 6因子(除草剂×剂量×基因型)设计3个重复,分别以0%、100%和200%的推荐剂量对氟美沙芬和伊马唑莫进行评价。收集植物视觉毒性、株高、茎粗、叶数和影像指标(植被提取绿色指数、超绿指数、超红指数和颜色指数)的数据。结果表明,三重互作显著,揭示了植物对除草剂反应的复杂性。典型判别分析解释了78.66%的总变异,其中第一个典型判别函数(29.42%)反映了结构发育和活力与胁迫的关系,第二个典型判别函数(27.59%)反映了植物的整体活力,第三个典型判别函数(21.65%)反映了胁迫和植物毒性对生长的负面影响。分析表明,基于图像的指标与多变量技术相结合,可以有效地量化植物毒性和区分基因型(对除草剂的耐受性和敏感性),克服了视觉评价的局限性,可以作为传统技术的补充工具。因此,亲本基因型IPR Campos Gerais和后代F1A对不同剂量的除草剂均有耐受性,而亲本基因型BAF36和后代F2B对不同剂量的除草剂均有敏感性。因此,所提出的方法对鉴定耐除草剂和敏感基因型是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Legume Science
Legume Science Agricultural and Biological Sciences-Plant Science
CiteScore
7.90
自引率
0.00%
发文量
32
审稿时长
6 weeks
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