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

2018 ◽  
Vol 95 ◽  
pp. 24-32 ◽  
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

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

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

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.

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

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.

scholarly journals Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

2018 ◽  
Author(s):  
Xiaqing Wang ◽  
Ruyang Zhang ◽  
Liang Han ◽  
Hao Yang ◽  
Wei Song ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Genome Wide Association Study ◽  
Early Stage ◽  
Growth Stages ◽  
Tropical Maize ◽  
Growth State ◽  
Aerial Vehicle ◽  
Trait Locus

AbstractPlant height is the key factor for plant architecture, biomass and yield in maize (Zea mays). In this study, plant height was investigated using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs) for maize diversity inbred lines at four important growth stages. Using an automated pipeline, we extracted accurate plant heights. We found that in temperate regions, from sowing to the jointing period, the growth rate for temperate maize was faster than tropical maize. However, from jointing to flowering stage, tropical maize maintained a vigorous growth state, and finally resulted in a taller plant than temperate lines. Genome-wide association study for temperate, tropical and both groups identified a total of 238 quantitative trait locus (QTLs) for the 16 plant height related traits over four growth periods. And, we found that plant height at different stages were controlled by different genes, for example, PIN1 controlled plant height at the early stage and PIN11 at the flowering stages. In this study, the plant height data collected by the UAV-HTTPs were credible and the genetic mapping power is high, indicating that the application of this UAV-HTTPs into the study of plant height will have great prospects.HighlightWe used UAV-based sensing platform to investigate plant height over 4 growth stages for different maize populations, and detected numbers of reliable QTLs using GWAS.

A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images

2021 ◽  
Vol 296 ◽  
pp. 108231
Author(s):  
Fusang Liu ◽  
Pengcheng Hu ◽  
Bangyou Zheng ◽  
Tao Duan ◽  
Binglin Zhu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Canopy Architecture ◽  
High Throughput Method ◽  
Aerial Vehicle

scholarly journals Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature

2020 ◽  
Vol 11 ◽  
Author(s):  
Gregor Perich ◽  
Andreas Hund ◽  
Jonas Anderegg ◽  
Lukas Roth ◽  
Martin P. Boer ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
Canopy Temperature ◽  
Field Phenotyping ◽  
Aerial Vehicle

The Design and Error Compensation Research of 3-Axis Electronic Compass Installed in Small Muti-Rotors Unmanned Aerial Vehicle

2012 ◽  
Vol 256-259 ◽  
pp. 2270-2273
Author(s):  
Song Wei Fan ◽  
Hong Wei Bian
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Error Compensation ◽  
Zero Bias ◽  
Unmanned Vehicle ◽  
Bias Adjustment ◽  
Self Calibration ◽  
Aerial Vehicle ◽  
Electronic Compass

A 3-axis electronic compass is designed for small multi-rotors unmanned vehicle. The STM32F103 is used as E-compass’ CPU, and ADXL345 and MAG3110 is used as the acceleration and geomagnetic sensor. The E-compass’ software is programmed by using IAR EWARM. For outdoor applications, the ellipsoid assumption theory is simply proved and used for E-compass’ self-calibration. By using the zero-bias adjustment for pre-calibration and the fitellipsoid compensation for precise calibration, the E-compass’ precision is nearly 1 degree.

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