Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN]
By Dijun Chen, Kerstin Neumann, Swetlana Friedel, B. Kilian, Ming Chen, Thomas Altmann, Christian Klukas. The Plant Cell, 2014
359 citations recorded by the source corpus.
A framework for high-throughput analysis of phenotypic traits from nondestructive plant imaging is used to dissect phenotypic components of barley growth and crop performance. Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.
Subfields: Ecology, Genetics, Plant Science
View Paper: https://www.semanticscholar.org/paper/3efa3435a6782b54a21840a2e4e8455b53ff2263
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Citations in the corpus
- Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, positive): “IAP was used to extract features from images and modelderived parameters to determine the drought response (DR) of barley (Chen et al., 2014) and screen the genetic variation of growth dynamics in maize in combination with GWASs (Muraya et al.”
- Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. (2015, methodology): “Several groups have demonstrated that plant pixel area from a single image stack can be used to calculate an approximate plant volume or total leaf area value that can accurately model fresh- and dry-weight above ground biomass of barley [16,17 ,19], rice [18 ,20], sorghum [21], and wheat [19].”
- Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions (2021, background): “[67] observed a significant difference in NIR reflectance in barley plants under drought conditions as NIR reflectance can be influenced by leaf thickness in addition to leaf water content [68].”
- Multi-modality imagery database for plant phenotyping (2016, methodology): “Near infrared (IR) reflectance at ∼ 940 nm has been used by others to detect water content in leaves [Chen et al., 2014].”
- Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research (2016, background): “In order to produce high-quality plant height growth curves capable of shedding light on differential growth of varieties under stress [39, 40], both systems would need improvement.”