Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.

By Noah Fahlgren, Malia A. Gehan, Ivan Baxter. Current opinion in plant biology, 2015

604 citations recorded by the source corpus.

Anticipated population growth, shifting demographics, and environmental variability over the next century are expected to threaten global food security. In the face of these challenges, crop yield for food and fuel must be maintained and improved using fewer input resources. In recent years, genetic tools for profiling crop germplasm has benefited from rapid advances in DNA sequencing, and now similar advances are needed to improve the throughput of plant phenotyping. We highlight recent developments in high-throughput plant phenotyping using robotic-assisted imaging platforms and computer vision-assisted analysis tools.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/d45fc9db10925afa299a4459603651ca55556241

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Citations in the corpus

Chlorophyll a fluorescence induction: Can just a one-second measurement be used to quantify abiotic stress responses? (2018, background): “Both attributes make incorporation of this measurement into high-throughput phenotyping platforms (see e.g. Fahlgren et al. 2015, Humplík et al. 2015) relatively straight forward; however, some issues might arise.”
Evaluating physiological responses of plants to salinity stress (2016, background): “Key words: Assessing salinity tolerance, salt-imposition systems, quantifying physiological traits, analysing salinity data, tolerance indices, salt stress phenotyping, osmotic stress.”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, methodology): “Phenotypes in (A,B) can be accurately measured using classical image processing techniques (Fahlgren et al., 2015b), while those in (C) are complex phenotyping tasks requiring more sophisticated analyses. images (Pawara et al., 2017).”
A Sustainable Agricultural Future Relies on the Transition to Organic Agroecological Pest Management (2018, background): “Recently, there have been massive advances in the fields of plant breeding and selection [141], phenotyping [142], metagenomics [143], and chemical ecology [144], which can leveraged for progress in organic agroecological sustainable pest management.”
Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, methodology): “5 Simulation study This section presents the results of a simulation study performed to evaluate the behaviour of our SpATS model under controlled scenarios, and its comparison with the separable autoregressive (AR×AR) model proposed by Gilmour et al. (1997). In the context of single-trial experimen”
Crop Phenomics: Current Status and Perspectives (2019, methodology): “…inbred lines (Seren et al., 2017); PhenoFront was a publicly available dataset of above-ground plant tissue to the LemnaTec Phenotyper platform (Fahlgren et al., 2015a); in 2016, the plant genomics and phenomics research data repository (PGP) were developed by the Leibniz institute of plant…”
Multi-modality imagery database for plant phenotyping (2016, background): “For this approach to be of effective, plants must be monitored continuously, over developmental time scales and under conditions that approximate the native environment (dynamic ‘field’ conditions) [12,34].”
Translating High-Throughput Phenotyping into Genetic Gain (2018, background): “The horizontal axis is partially redrawn from [23].”
Morphological, Physiological, and Genetic Responses to Salt Stress in Alfalfa: A Review (2020, background): “Imaging within the visible spectrum (~400–700 nm) can measure the morphological and color properties of plants, while hyperspectral imaging can also study the radiative properties of plant leaves to facilitate the early detection of abiotic stress [98,99].”
Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (2019, methodology): “Besides the non-invasive manner of these technologies, it is a further advantage that image assessment and analysis can be performed in a fraction of time compared to common methods and in high throughput [20,21].”