Plant Phenotyping: Past, Present, and Future

Pieruschka, Roland; Schurr, Uli

doi:10.1155/2019/7507131

Cited by 47 publications

(35 citation statements)

References 37 publications

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“…Internationally available platforms have automated delivery of plants to imaging technologies for time lapse studies ( [44], reviewed in [38] and [46]). The throughput of the available 4D root imaging technologies depends on pot size, soil type, detection method, and instrument automation and design.…”

Section: Functional and Dynamic Phenotypes For Capture Of Soil Resourcesmentioning

confidence: 99%

“…An exciting multi-lab approach has been initiated, using repeatable, 3D printed microcosms called Eco-Fabs ( Figure 5C) [109]. Bespoke phenotyping platforms are available globally in fixed locations with highly specialized operators for researchers, breeders, and prebreeders [46]. It is predicted that mobile, easy to build systems to test cross-lab repeatability, will be a new addition to plant and rhizosphere phenotyping in future.…”

Section: Where To Next: Fields Of Discovery In the Rhizosphere That Imentioning

confidence: 99%

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Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Tracy

Nagel

Postma

et al. 2020

Trends in Plant Science

182

128

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Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today's phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.Root system traits (see Glossary) have long been a key target by researchers and breeders for crop improvement [1,2]. Root system architecture supplies water and nutrients for photosynthesis and growth, stabilizes the plant, and prevents soil toxic elements and pathogens from entering leaves and reproductive organs. Roots also host soil microorganisms that can contribute to plant growth and resource efficiency and modulate the supply of resources and signals to shoots, influencing partitioning to organs, including flowers, seed, and fruit. Despite the challenges that plant roots present for measurement, root traits have been incorporated into crops using phenotyping. The observation of roots using phenotyping is central to the discovery of root traits beneficial to crops, their incorporation into new cultivars using prebreeding [3,4], and to their management using precision agriculture. Highlights in Root PhenotypingThe 11 programs highlighted in Figure 1 (Key Figure) exemplify root traits incorporated into new genotypes by phenotyping to increase crop productivity. The examples cover the two main plant types and root system developments: monocotyledons (two major cereal crops, wheat and rice) with seed and stem-borne roots with no cambial thickening, and dicotyledons (two major legume crops, bean

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Section: Functional and Dynamic Phenotypes For Capture Of Soil Resourcesmentioning

confidence: 99%

Section: Where To Next: Fields Of Discovery In the Rhizosphere That Imentioning

confidence: 99%

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Tracy

Nagel

Postma

et al. 2020

Trends in Plant Science

182

128

View full text Add to dashboard Cite

show abstract

“…g n xy log ĝ n xy + 1 -g n xy log 1 -ĝ n xy , (1) where for N features,ĝ n xy is the predicted class output at location (x, y), and g n xy is the ground truth prediction at that location. The weight of each class is scaled by it's frequency relative to the median frequency of all classes by α c , given by:…”

Section: Loss Functionsmentioning

confidence: 99%

“…Plant phenotyping plays a key role in plant science research, underpinning large-scale genetic discovery, and the breeding of more resilient traits [1]. This innovation makes a fundamental contribution to the push for global food security.…”

Section: Introductionmentioning

confidence: 99%

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

Yasrab

Atkinson

Wells

et al. 2019

Preprint

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We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and rst and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to di erent plant species via transfer learning, o ering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a nal time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.

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“…Plant phenotype and genotyping are terms associated with understanding plant growth and the effects of environmental conditions that influence the plant growth [1]. Plant phenotyping is an evolving biosciences area that helps to connect the plant genomics with agronomy and ecophysiology [2]. It is the study of biochemical and physical appears and characteristics of plants [3].…”

Section: Introductionmentioning

confidence: 99%

PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System

Yasrab

Pound

French

et al. 2020

Preprint

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This research will explore the phenotype-genotype gap by bringing two very diverse technologies together to predict plant characteristics. Currently, there are several studies and tools available for plant phenotype and genotype analysis. However, there is no existing single system that offers both capabilities in one package. Usually, Convolution Neural Networks used for plant phenotyping analysis and Recurrent Neural Networks used for genotype analysis. Both of these machine leanring methods require different input data for feature extraction, analysis and learning. Building a machine learning system for plant data that can make use of both graphic (for phenotype) and time-series (for genotype) is critical and challenging, especially when the system has to predict sensitive information regarding plant growth, accession and types. In this study, the proposed system will solve these problems by bringing two very different technologies, analysis methods and datasets. The proposed research aims to bridge the phenotype-genotype gap using CNN-LSTMs to process graphic and temporal data of plant roots. The proposed system "PhenomNet" offers segmentation of plant roots along with the classification of the given dataset into different accessions. The experiment results have shown that proposed CNN-LSTM architecture provides very high accuracy in comparison to manual or semi-automated approaches.

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Plant Phenotyping: Past, Present, and Future

Cited by 47 publications

References 37 publications

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System

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