» Articles » PMID: 28713402

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Overview
Journal Front Plant Sci
Date 2017 Jul 18
PMID 28713402
Citations 123
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Phenology analysis for trait prediction using UAVs in a MAGIC rice population with different transplanting protocols.

Taniguchi S, Sakamoto T, Nakamura H, Nonoue Y, Guan D, Fukuda A Front Artif Intell. 2025; 7:1477637.

PMID: 39917549 PMC: 11799559. DOI: 10.3389/frai.2024.1477637.


Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.

Sarkar S, Osorio Leyton J, Noa-Yarasca E, Adhikari K, Hajda C, Smith D Sensors (Basel). 2025; 25(2.

PMID: 39860919 PMC: 11769266. DOI: 10.3390/s25020543.


Seasonal monitoring of forage C:N:ADF ratio in natural rangeland using remote sensing data.

Rapiya M, Ramoelo A, Truter W Environ Monit Assess. 2025; 197(2):137.

PMID: 39762621 PMC: 11703936. DOI: 10.1007/s10661-024-13579-x.


High throughput phenotyping in soybean breeding using RGB image vegetation indices based on drone.

Alves A, Araujo M, Chaves S, Dias L, Corredo L, Pessoa G Sci Rep. 2024; 14(1):32055.

PMID: 39738482 PMC: 11685969. DOI: 10.1038/s41598-024-83807-4.


Early detection of pine wilt disease based on UAV reconstructed hyperspectral image.

Liu W, Xie Z, Du J, Li Y, Long Y, Lan Y Front Plant Sci. 2024; 15:1453761.

PMID: 39628534 PMC: 11611544. DOI: 10.3389/fpls.2024.1453761.


References
1.
Araus J, Cairns J . Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 2013; 19(1):52-61. DOI: 10.1016/j.tplants.2013.09.008. View

2.
Jones H, Serraj R, Loveys B, Xiong L, Wheaton A, Price A . Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct Plant Biol. 2020; 36(11):978-989. DOI: 10.1071/FP09123. View

3.
Yu K, Kirchgessner N, Grieder C, Walter A, Hund A . An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping. Plant Methods. 2017; 13:15. PMC: 5361853. DOI: 10.1186/s13007-017-0168-4. View

4.
Gonzalez-Recio O, Coffey M, Pryce J . On the value of the phenotypes in the genomic era. J Dairy Sci. 2014; 97(12):7905-15. DOI: 10.3168/jds.2014-8125. View

5.
Grieder C, Hund A, Walter A . Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature. Funct Plant Biol. 2020; 42(4):387-396. DOI: 10.1071/FP14226. View