Phenomics--technologies to relieve the phenotyping bottleneck.

By Robert Furbank, Mark A. Tester. Trends in Plant Science, 2011

1,501 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a07a8874568532882e25f34dd247258f22669e53

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Citations in the corpus

Multi-modality imagery database for plant phenotyping (2016, neutral): “Keywords Plant Phenotyping · Computer Vision · Plant image · Leaf segmentation · Leaf tracking · Multiple sensors · Arabidopsis · Bean”
Translating High-Throughput Phenotyping into Genetic Gain (2018, background): “Trait phenotyping and crop growth models are evolving to the point where breeders can access mechanistic information on the physiological determinants of plant adaptation for precise selection of cultivars suitable for the target environment [13].”
Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (2019, background): “Most prominently, there is a bottleneck since the capacity and the throughput during the traditional phenotyping process by human experts is still comparatively low [11,12 ].”
Field Phenotyping of Soybean Roots for Drought Stress Tolerance (2014, background): “Moreover, despite intensive efforts, systems that are directly applicable in the field remain largely at the prototype stage [2,3].”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “The relatively recent availability of high-throughput crop phenotyping methods has influenced developments in crop selection and improvement [5], [6], and previous reviews have presented new opportunities for using ground and aerial phenotyping platforms that carry different sensors [7], [8].”
Plant phenotyping: from bean weighing to image analysis (2015, background): “Throughout the last decade, the terms phenotyping and phenomics have more and more often been linked to non-destructive optical analyses of plant traits based on images [8,9].”
Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping (2017, background): “However, cultivar selection requires a deep understanding of how the plant genetic makeup (genotype) relates to the observable plant traits (phenotypes), and how the genes express themselves in a given environment (Furbank and Tester, 2011).”
Image-based plant phenotyping with incremental learning and active contours (2014, background): “However, as of today, inexpensive and automated phenotyping (phenomics) remains a bottleneck (Furbank and Tester, 2011; Spalding and Miller, 2013).”