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Plant phenotyping: increasing throughput and precision at multiple scales

Functional plant biology : FPB · 1 Feb 2016 · 10.1071/fpv44n1_fo

Abstract

In this special issue of Functional Plant Biology, we present a perspective of the current state of the art in plant phenotyping. The applications of automated and detailed recording of plant characteristics using a range of mostly non-invasive techniques are described. Papers range from tissue scale analysis through to aerial surveying of field trials and include model plant species such as Arabidopsis as well as commercial crops such as sugar beet and cereals. The common denominators are high throughput measurements, data rich analyses often utilising image based data capture, requirements for validation when proxy measurement are employed and in many instances a need to fuse datasets. The outputs are detailed descriptions of plant form and function. The papers represent technological advances and important contributions to basic plant biology, and these studies are commonly multidisciplinary, involving engineers, software specialists and plant physiologists. This is a fast moving area producing large datasets and analytical requirements are often common between very diverse platforms.

Plant phenotyping relevance

植物フェノタイピングの現状を、非侵襲的・自動・高スループットな計測技術、画像データ取得、検証、データ融合の観点から概説するレビューであり、方法論が中心です。

abstractwe present a perspective of the current state of the art in plant phenotyping.
abstractThe common denominators are high throughput measurements, data rich analyses often utilising image based data capture, requirements for validation when proxy measurement are employed and in many instances a need to fuse datasets.

Code and data availability

This is a two-page foreword to a special issue on plant phenotyping. It only summarizes and cites the contributing papers; no phenotype datasets, images, code, models, or supplements specific to this paper are described, and no availability statements or URLs for such assets appear.

No evidence-backed public reproduction asset is currently recorded.

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