ArticleBreeding science2022
Improving the efficiency of plant root system phenotyping through digitization and automation.
Article in Breeding science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Rice cycles between drought and well-watered-adapted phenotypes by changing lateral root formation.Annals of botany · 2025Article
- Resequencing and phenotyping of the first highly inbred eggplant multiparent population revealHorticulture research · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.Plant methods · 2024Article
- Characterizing a Cost-Effective Hydrogel-Based Transparent Soil.Gels (Basel, Switzerland) · 2023Article
- Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction.Plant methods · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.Frontiers in plant science · 2022Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.
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