ReviewAnnals of botany2012
Advanced phenotyping offers opportunities for improved breeding of forage and turf species.
Review in Annals of botany, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed.
- Genetic Variation of Growth Traits and Seed Production in a Patagonian Native Pasture in Semiarid Rangelands Under Different Environmental Settings.Plants (Basel, Switzerland) · 2025Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- Article
- Prospects forFrontiers in plant science · 2021Review
- Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning.Plant phenomics (Washington, D.C.) · 2021Article
- High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance.PloS one · 2021Article
- Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks.Frontiers in plant science · 2020Article
- Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks.Frontiers in plant science · 2020Article
- Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.Frontiers in plant science · 2020Article
- All roads lead to growth: imaging-based and biochemical methods to measure plant growth.Journal of experimental botany · 2020Article
- Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Plant methods · 2019Article
- A low-cost and open-source platform for automated imaging.Plant methods · 2019Article
- Improving the Yield and Nutritional Quality of Forage Crops.Frontiers in plant science · 2018Review
- Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model.Plant methods · 2018Article
- Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis.Journal of integrative bioinformatics · 2017Article
- Non-destructive Phenotyping to IdentifyFrontiers in plant science · 2017Article
- High-throughput phenotyping of lateral expansion and regrowth of spaced Lolium perenne plants using on-field image analysis.Plant methods · 2016Article
- Large Variability of Proanthocyanidin Content and Composition in Sainfoin (Onobrychis viciifolia).Journal of agricultural and food chemistry · 2015Article
- Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time.Journal of experimental botany · 2015Article
- Rapid and efficient estimation of pea resistance to the soil-borne pathogen Fusarium oxysporum by infrared imaging.Sensors (Basel, Switzerland) · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsAdvanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources. SCOPE: This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed.
conclusionsProgress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.