ArticlePlant phenomics (Washington, D.C.)2021
Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series.
Article in Plant phenomics (Washington, D.C.), 2021. 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.
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements.Plant phenomics (Washington, D.C.) · 2025Article
- StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.Frontiers in plant science · 2025Article
- Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops.Planta · 2024Review
- Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data.Plant phenomics (Washington, D.C.) · 2024Article
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.Frontiers in plant science · 2024Article
- Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.Plant phenomics (Washington, D.C.) · 2024Article
- Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing.Plant methods · 2023Article
- Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field.Research (Washington, D.C.) · 2023Article
- Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.Plant phenomics (Washington, D.C.) · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.
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Registered trials
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