ArticlePloS one2016
Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Improved Transformer for Time Series Senescence Root Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies.Plant phenomics (Washington, D.C.) · 2023Article
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
- Interspecific and intraspecific phenotypic diversity for drought adaptation in bioenergyGlobal change biology. Bioenergy · 2021Article
- High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance.PloS one · 2021Article
- High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response.Journal of experimental botany · 2020Article
- Image-Based Assessment of Drought Response in Grapevines.Frontiers in plant science · 2020Article
- Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone.The New phytologist · 2019Article
- Crop Phenomics: Current Status and Perspectives.Frontiers in plant science · 2019Review
- instaGraminoid, a Novel Colorimetric Method to Assess Herbicide Resistance, Identifies Patterns of Cross-Resistance in Annual Ryegrass.Plant phenomics (Washington, D.C.) · 2019Article
- Systems biology of seeds: decoding the secret of biochemical seed factories for nutritional security.3 Biotech · 2018Review
- Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images.PloS one · 2018Article
- Improving Salt Tolerance of Chickpea Using Modern Genomics Tools and Molecular Breeding.Current genomics · 2017Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.
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