ReviewFrontiers in plant science2017
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives.
Review in Frontiers in plant science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
157 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Exploring and exploiting genetics and genomics for sweetpotato improvement: Status and perspectives.Plant communications · 2022Pooled it
- Crop legacies as genetic targets for sustainable farming systems.Nature genetics · 2026Review
- PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants.Plant phenomics (Washington, D.C.) · 2026Article
- A renewed focus on carbon uptake and use to enhance crop performance in the face of rising temperatures.Plant physiology · 2026Review
- Drone-based microclimate analytics reveal vegetation-shade interaction as a key environmental drivers of Aedes-prone microhabitat suitability.Scientific reports · 2026Article
- Recent Developments and Applications of Drone Swarm: Techniques, Strategies, and Challenges.Sensors (Basel, Switzerland) · 2026Review
- A Uav-based multisensor framework for legal industrial Cannabis monitoring and open-access dataset development.Data in brief · 2026Article
- High-throughput estimation of sugarcane phenotypic traits using UAV multispectral data under high-density planting conditions.Plant phenomics (Washington, D.C.) · 2026Article
- 3D reconstruction analysis of maize-soybean intercropping competition under water stress.Plant phenomics (Washington, D.C.) · 2026Article
- Development and Field Validation of a Smartphone-Based Web Application for Diagnosing Optimal Timing of Mid-Season Drainage in Rice Cultivation via Canopy Image-Derived Tiller Estimation.Sensors (Basel, Switzerland) · 2026Article
- Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India.Frontiers in artificial intelligence · 2026Review
- Multitask ST-LSTM model based on UAV hyperspectral remote sensing for wheat yield prediction.Frontiers in plant science · 2026Article
- UAV-based phenotyping outperforms visual canopy wilting for evaluating soybean drought tolerance and yield retention under rainfed conditions.Frontiers in plant science · 2026Article
- Explainable machine learning to predict root biomass of field crops using UAV multispectral data.Frontiers in plant science · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- The regulatory effects of water-fertilizer integration on maize canopy uniformity and yield: a comprehensive evaluation based on multi-source UAV data.Frontiers in plant science · 2026Article
- A dual-branch perception and hybrid attention integrated framework for temporal remote estimation of wheat leaf biomass.Frontiers in plant science · 2026Article
- Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.Frontiers in plant science · 2026Article
- Prediction of growth parameters in the medicinal plantFrontiers in plant science · 2026Article
97 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.
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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.