ReviewPlant phenomics (Washington, D.C.)2021
UAS-Based Plant Phenotyping for Research and Breeding Applications.
Review 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 47 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 123 citations in OpenAlex.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Advancing multi-scale plant phenotyping for precision agriculture and sustainable crop production.Journal of Zhejiang University. Science. B · 2026Review
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- PMSPA-Net framework with geometric region decomposition for high-precision phenotyping of field-grown maize seedlings.Frontiers in plant science · 2026Article
- Multitask ST-LSTM model based on UAV hyperspectral remote sensing for wheat yield prediction.Frontiers in plant science · 2026Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond.Plant phenomics (Washington, D.C.) · 2025Review
- Identifying canopy wilting QTLs and evaluating remote sensing approaches for selecting drought-tolerant soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Generation of labeled leaf point clouds for plants trait estimation.Plant phenomics (Washington, D.C.) · 2025Article
- Historic insights and future potential in wheat elaborated using a diverse cultivars collection and extended phenotyping.Scientific reports · 2025Article
- Maui: modular analytics of UAS imagery for specialty crop research.Plant methods · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- SHP Buddy: a QGIS plugin for generating shapefiles to support remote sensing in plant breeding and agronomic experiments.Plant methods · 2025Article
- Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis.Journal of imaging · 2025Article
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- Broadband Normalized Difference Reflectance Indices and the Normalized Red-Green Index as a Measure of Drought in Wheat and Pea Plants.Plants (Basel, Switzerland) · 2024Article
- Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.Plant phenome journal · 2024Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
Identifiers
What OpenQuestion holds
Registered trials
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.