ArticleThe New phytologist2022
AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.
Article in The New phytologist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 30 citations in OpenAlex.
- ELMERF: A deep-learning-assisted hydroponic RGB phenotyping framework for rice seedling salt-stress evaluation and genetic mapping.Plant phenomics (Washington, D.C.) · 2026Article
- BloomSight: An ultra-high-frequency phenotyping framework for diurnal flowering dynamics inPlant phenomics (Washington, D.C.) · 2026Article
- Root-TransUNet enables high-throughput phenotyping ofFrontiers in plant science · 2026Article
- UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut (Plant phenomics (Washington, D.C.) · 2025Article
- OrchardQuant-3D: combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards.Plant biotechnology journal · 2025Article
- Precise Image Color Correction Based on Dual Unmanned Aerial Vehicle Cooperative Flight.Plant phenomics (Washington, D.C.) · 2025Article
- Segmenting vegetation from UAV images via spectral reconstruction in complex field environments.Plant phenomics (Washington, D.C.) · 2025Article
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- EasyDAM_V4: Guided-GAN-based cross-species data labeling for fruit detection with significant shape difference.Horticulture research · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- IHUP: An Integrated High-Throughput Universal Phenotyping Software Platform to Accelerate Unmanned-Aerial-Vehicle-Based Field Plant Phenotypic Data Extraction and Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.Plant phenomics (Washington, D.C.) · 2024Article
- Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.Plant phenomics (Washington, D.C.) · 2024Article
- From Images to Loci: Applying 3D Deep Learning to Enable Multivariate and Multitemporal Digital Phenotyping and Mapping the Genetics Underlying Nitrogen Use Efficiency in Wheat.Plant phenomics (Washington, D.C.) · 2024Article
- High-Throughput Yield Prediction of Diallele Crossed Sugar Beet in a Breeding Field Using UAV-Derived Growth Dynamics.Plant phenomics (Washington, D.C.) · 2024Article
- CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones.Frontiers in plant science · 2023Article
- Phenomic and genomic prediction of yield on multiple locations in winter wheat.Frontiers in genetics · 2023Article
- Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.Plant phenomics (Washington, D.C.) · 2023Article
- The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.Plant phenomics (Washington, D.C.) · 2023Article
- What can aerial phenotyping do and bring to us (breeders)?The New phytologist · 2022Article
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13 authors at 4 institutions in 2 countries.
Funding
Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.