Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Remote Sensing ◽

10.3390/rs12030574 ◽

2020 ◽

Vol 12 (3) ◽

pp. 574 ◽

Cited By ~ 2

Author(s):

Yuncai Hu ◽

Samuel Knapp ◽

Urs Schmidhalter

Keyword(s):

Grain Yield ◽

Plant Breeding ◽

High Throughput ◽

Complex Traits ◽

New Technologies ◽

Early Selection ◽

Spectral Indices ◽

Wheat Genotypes ◽

High Throughput Phenotyping ◽

Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

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High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling

Frontiers in Plant Science ◽

10.3389/fpls.2017.00421 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 91

Author(s):

Kakeru Watanabe ◽

Wei Guo ◽

Keigo Arai ◽

Hideki Takanashi ◽

Hiromi Kajiya-Kanegae ◽

...

Keyword(s):

Plant Height ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Genomic Prediction ◽

Prediction Modeling ◽

Sorghum Plant ◽

Aerial Vehicle ◽

High Throughput Phenotyping

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Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

Remote Sensing ◽

10.3390/rs13061187 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1187

Author(s):

Rubén Rufo ◽

Jose Miguel Soriano ◽

Dolors Villegas ◽

Conxita Royo ◽

Joaquim Bellvert

Keyword(s):

Bread Wheat ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Agronomic Traits ◽

Stepwise Multiple Regression ◽

Area Index ◽

Rainfed Conditions ◽

Aerial Vehicle ◽

High Throughput Phenotyping ◽

Rgb Images

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.

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Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding

European Journal of Agronomy ◽

10.1016/j.eja.2018.02.004 ◽

2018 ◽

Vol 95 ◽

pp. 24-32 ◽

Cited By ~ 46

Author(s):

Pengcheng Hu ◽

Scott C. Chapman ◽

Xuemin Wang ◽

Andries Potgieter ◽

Tao Duan ◽

...

Keyword(s):

Plant Height ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Self Calibration ◽

Aerial Vehicle ◽

High Throughput Phenotyping ◽

Sorghum Breeding

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Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Functional Plant Biology ◽

10.1071/fp20309 ◽

2021 ◽

Author(s):

Pengcheng Hu ◽

Scott C. Chapman ◽

Bangyou Zheng

Keyword(s):

Machine Learning ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Learning Methods ◽

Machine Learning Methods ◽

Aerial Vehicle ◽

High Throughput Phenotyping ◽

Ground Coverage

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Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Scientific Reports ◽

10.1038/s41598-021-82797-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhou Tang ◽

Atit Parajuli ◽

Chunpeng James Chen ◽

Yang Hu ◽

Samuel Revolinski ◽

...

Keyword(s):

Prediction Model ◽

High Throughput ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Selection Process ◽

Forage Legume ◽

Breeding Programs ◽

High Throughput Phenotyping ◽

Fully Automatic ◽

Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

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