scholarly journals High‐Throughput Field Phenotyping to Assess Irrigation Treatment Effects in Quinoa

age ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Carlos Zúñiga Espinoza ◽  
Leonardo Hinojosa ◽  
Xiaodan Ma ◽  
Kevin Murphy
Keyword(s):  
High Throughput ◽  
Treatment Effects ◽  
Irrigation Treatment ◽  
Field Phenotyping

scholarly journals Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Tom De Swaef ◽  
Wouter H. Maes ◽  
Jonas Aper ◽  
Joost Baert ◽  
Mathias Cougnon ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
Lolium Perenne ◽  
High Throughput ◽  
Festuca Arundinacea ◽  
Stress Index ◽  
Forage Grasses ◽  
Crop Water Stress Index ◽  
Ploidy Levels ◽  
Field Phenotyping

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

scholarly journals High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

2016 ◽  
Vol 8 (12) ◽  
pp. 1031 ◽  
Author(s):  
Fenner Holman ◽  
Andrew Riche ◽  
Adam Michalski ◽  
March Castle ◽  
Martin Wooster ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Rate ◽  
Plant Height ◽  
High Throughput ◽  
Wheat Plant ◽  
Field Plot ◽  
Field Phenotyping

scholarly journals Infra-Red Thermography as a High-Throughput Tool for Field Phenotyping

Agronomy ◽  
2014 ◽  
Vol 4 (3) ◽  
pp. 397-417 ◽  
Author(s):  
Ankush Prashar ◽  
Hamlyn Jones
Keyword(s):  
High Throughput ◽  
Infra Red ◽  
Field Phenotyping

Investigating the potential of satellite imagery for high-throughput field phenotyping applications

2020 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Chongyuan Zhang ◽  
Preston Hurst ◽  
Afef Marzougui ◽  
Arun Narenthiran Veeranampalayam Sivakumar ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
High Throughput ◽  
Field Phenotyping

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

Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping

2021 ◽  
Vol 189 ◽  
pp. 106380
Author(s):  
Norman Wilke ◽  
Bastian Siegmann ◽  
Johannes A. Postma ◽  
Onno Muller ◽  
Vera Krieger ◽  
...  
Keyword(s):  
High Throughput ◽  
Plant Density ◽  
Multispectral Imagery ◽  
Field Phenotyping

scholarly journals High-throughput field phenotyping of soybean: Spotting an ideotype

2021 ◽  
pp. 112797
Author(s):  
Lukas Roth ◽  
Christoph Barendregt ◽  
Claude-Alain Bétrix ◽  
Andreas Hund ◽  
Achim Walter
Keyword(s):  
High Throughput ◽  
Field Phenotyping

scholarly journals Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems

2020 ◽  
Author(s):  
Xingche Guo ◽  
Yumou Qiu ◽  
Dan Nettleton ◽  
Cheng-Ting Yeh ◽  
Zihao Zheng ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Traits ◽  
Network Models ◽  
Modern Technology ◽  
Neural Network Models ◽  
Novel Approach ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Rich Data

ABSTRACTHigh-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.

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