Editorial: Plant Phenotyping and Phenomics for Plant Breeding

Lobos, Germán; Camargo, Anyela; Pozo, Alejandro del; Araus, José Luís; Ortíz, Rodomiro; Doonan, John H.

doi:10.3389/fpls.2017.02181

Cited by 66 publications

(46 citation statements)

References 12 publications

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“…Plant phenotyping based on UAV remote sensing is coming to an increasing popularity in breeding programs for evaluating and selecting crop varieties for improved yield (Sankaran et al, 2015;Lobos et al, 2017). Here we assessed patterns of genotypic variability in functional traits of adult trees, which is a fundamental step towards the assessment of the adaptive potential of forest species to environmental changes (Bussotti et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, high-throughput phenotyping tools have been developed in plant sciences that enable a straightforward evaluation of hundreds of individuals with reduced economic and time costs (Großkinsky et al, 2015;Lobos et al, 2017). In this regard, remote sensing imagery acquired with unmanned aerial vehicles (UAVs) allows for an efficient morphophysiological characterization of a large number of experimental units (Tattaris et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Using unmanned aerial vehicle‐based multispectral, RGB and thermal imagery for phenotyping of forest genetic trials: A case study inPinus halepensis

Santini

Kefauver

Dios

et al. 2019

Annals of Applied Biology

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negatively), indicating a strong influence of canopy properties and stomatal conductance on above-ground growth at the population level. Specifically, a combination of vegetation indices and canopy temperature accounted for about 60% of population variability in stem volume of adult trees. This is the first study to propose UAV remote sensing as an effective tool for screening genetic variation in morphophysiological traits of adult forest trees.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle‐based multispectral, RGB and thermal imagery for phenotyping of forest genetic trials: A case study inPinus halepensis

Santini

Kefauver

Dios

et al. 2019

Annals of Applied Biology

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show abstract

“…Recent advances in NGS technologies, computational analysis [5], quantitative genetics [6,7], genomics [8,9], phenomics [10,11] offer the opportunity to enhance crop selection programs through a multidisciplinary process based on data enrichment [12,13]. Array-based genotyping platforms and reduced representation-based methods such as genotyping by sequencing (GBS) or restriction-site associated DNA sequencing (RAD-seq) [14][15][16] drastically reduced the time and costs of analysis, paving a rapid way for genetic diversity studies, quantitative trait loci (QTLs) dissection and identification of genes underpinning traits of agricultural interest.…”

Section: Genomics Applied To Breeding: What We Gain From Short Readsmentioning

confidence: 99%

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Esposito¹,

Carputo

Cardi³

et al. 2019

Plants

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Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.

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“…Improved nutrient content and WP can be accomplished by breeding water/nutrient -efficient crops. The International Center for Agricultural Research in the Dry Areas (ICARDA) along with internationally recognized academic breeding centers has established a solid platform for WP breeding (Dwivedi, Britt et al 2015;Ortiz 2015;Lobos, Camargo et al 2017;Valdiani, Talei et al 2017). Selected genotypes are better suited to cope with environmental fluctuations and extremes such as drought and heat spells.…”

Section: Introductionmentioning

confidence: 99%

“…Selected genotypes are better suited to cope with environmental fluctuations and extremes such as drought and heat spells. Whole-genome prediction models estimate all marker effects in all loci and capture small quantitative trait loci (QTL) mapping effects are applied have resulted in impressive advances and became routine in agricultural breeding (Dwivedi, Britt et al 2015;Ortiz 2015;Lobos, Camargo et al 2017;Valdiani, Talei et al 2017). At the phenotype level, improved agricultural techniques and novel highthroughput selection systems are being developed to enable rapid pre-field screening for specific traits.…”

Section: Introductionmentioning

confidence: 99%

The plant microbiome as a resource to increase crop productivity and soil resilience: A systems approach

Timmusk¹,

Zucca²

2019

J. Cam. Acad. Sci

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Climate change along with global population increase pose a challenge to worldwide crop production and soil health. There is a need to intensify agricultural production in a sustainable manner and to find solutions to combat abiotic and biotic stress situations. Plant roots can be colonized by a variety of favorable species and genera that promote plant growth. A systems approach to integrating plant breeding and microbiome via applying novel molecular tools, screening technologies and precision phenotyping has the potential to advance the microbial reproducible application under natural conditions. ResumeLe changement climatique ainsi que l'augmentation de la population mondiale posent un défi pour la production agricole mondiale et la santé des sols. Il est nécessaire d'intensifier durablement la production agricole et de trouver des solutions pour lutter contre les situations de stress abiotiques et biotiques. Les racines des plantes peuvent être colonisées par une variété d'espèces et de genres favorables qui favorisent la croissance des plantes. Une approche systémique d'intégration de la sélection végétale et du microbiome via l'application de nouveaux outils moléculaires, de technologies de criblage et d'un phénotypage de précision pourrait faire progresser l'application reproductible microbienne dans des conditions naturelles

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Editorial: Plant Phenotyping and Phenomics for Plant Breeding

Cited by 66 publications

References 12 publications

Using unmanned aerial vehicle‐based multispectral, RGB and thermal imagery for phenotyping of forest genetic trials: A case study inPinus halepensis

Using unmanned aerial vehicle‐based multispectral, RGB and thermal imagery for phenotyping of forest genetic trials: A case study inPinus halepensis

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

The plant microbiome as a resource to increase crop productivity and soil resilience: A systems approach

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