2017
DOI: 10.1002/cppb.20044
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Whole‐Plant Manual and Image‐Based Phenotyping in Controlled Environments

Abstract: Phenotypic measurements and images of crops grown under controlled‐environment conditions can be analyzed to compare plant growth and other phenotypes from diverse varieties. Those demonstrating the most favorable phenotypic traits can then be used for crop improvement strategies. This article details a protocol for image‐based root and shoot phenotyping of plants grown in the greenhouse to compare traits among different varieties. Diverse maize lines were grown in the greenhouse in large 8‐gallon treepots in … Show more

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Cited by 6 publications
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“…The lack of efficient and accurate phenotyping protocols has been a limiting factor in further genetic dissection of culm architecture for example through exploration of wider genetic diversity in germplasm collections. In this context, different solutions emerged in recent years relying on high-throughput phenotyping methods based on the use of new image analysis tools with advanced software and special platforms (Agnew et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…The lack of efficient and accurate phenotyping protocols has been a limiting factor in further genetic dissection of culm architecture for example through exploration of wider genetic diversity in germplasm collections. In this context, different solutions emerged in recent years relying on high-throughput phenotyping methods based on the use of new image analysis tools with advanced software and special platforms (Agnew et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Computer‐based automated image analysis is an alternative method for classifying plant phenotypes quantitatively (Furbank and Tester, 2011 ; Das et al, 2015 ; Fahlgren et al, 2015 ; Agnew et al, 2017 ; Tovar et al, 2018 ; Seethepalli et al, 2020 ). Root system markup language (RSML) software is generally designed for the analysis of the whole root system for root traits such as area, depth, or diameter (Lobet et al, 2015 ; Ndour et al, 2017 ; Shahzad et al, 2018 ; Yasrab et al, 2019 ).…”
mentioning
confidence: 99%