scholarly journals High‐throughput drone‐based remote sensing reliably tracks phenology in thousands of conifer seedlings

2020 ◽  
Vol 226 (6) ◽  
pp. 1667-1681 ◽  
Author(s):  
Petra D'Odorico ◽  
Ariana Besik ◽  
Christopher Y. S. Wong ◽  
Nathalie Isabel ◽  
Ingo Ensminger
Keyword(s):  
Remote Sensing ◽  
High Throughput ◽  
Conifer Seedlings

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

High-Throughput, Resource-Efficient Multi-Dimensional Parallel Architecture for Space-Borne Sea-Land Segmentation

2020 ◽  
pp. 2150027
Author(s):  
Cunguang Zhang ◽  
Hongxun Jiang ◽  
Riwei Pan ◽  
Haiheng Cao ◽  
Mingliang Zhou
Keyword(s):  
Remote Sensing ◽  
High Throughput ◽  
Large Scale ◽  
Parallel Architecture ◽  
Remote Sensing Image ◽  
Satellite System ◽  
Peak Performance ◽  
Remote Sensing Images ◽  
Block Level ◽  
Remote Sensing Image Processing

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access; a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on Xilinx Virtex k7-410t to find that its practical processing time for a [Formula: see text] remote sensing image is only about 0.4[Formula: see text]s. The peak performance is 1.625[Formula: see text]gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.

Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry

2016 ◽  
Author(s):  
David Gouache ◽  
Katia Beauchêne ◽  
Agathe Mini ◽  
Antoine Fournier ◽  
Benoit de Solan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Throughput ◽  
Crop Improvement ◽  
Scale Up ◽  
Field Phenotyping

scholarly journals Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping

2017 ◽  
Author(s):  
F. Baret ◽  
S. Madec ◽  
K. Irfan ◽  
J. Lopez ◽  
A. Comar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Throughput ◽  
Canopy Structure ◽  
Leaf Rolling ◽  
Hemispherical Photography ◽  
Structure Changes ◽  
Field Phenotyping ◽  
Leaf Level ◽  
Intercepted Radiation

AbstractLeaf rolling in maize crops is one of the main plant reactions to water stress that may be visually scored in the field. However, the leaf scoring did not reach the high-throughput desired by breeders for efficient phenotyping. This study investigates the relationship between leaf rolling score and the induced canopy structure changes that may be accessed by high-throughput remote sensing techniques.Results gathered over a field phenotyping platform run in 2015 and 2016 show that leaf starts to roll for the water stressed conditions around 9:00 and reaches its maximum around 15:00. Conversely, genotypes conducted under well watered conditions do not show any significant rolling during the same day. Leaf level rolling was very strongly correlated to canopy structure changes as described by the fraction of intercepted radiation fIPARWS derived from digital hemispherical photography. The changes in fIPARWS were stronly correlated (R2=0.86, n=50) to the leaf level rolling visual score. Further, a very good consistency of the genotype ranking of the fIPARWS changes during the day was found (ρ=0.62). This study demonstrating the strong coordination between leaf level rolling and its impact on canopy structure changes poses the basis for new high-throughput remote sensing methods to quantify this water stress trait.HighlighThe diurnal dynamics of leaf rolling scored visually is strongly related to canopy structure changes that can be documented using Digital hemispherical photography. Consequences for high-throughput field phenotyping are discussed

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

2011 ◽  
Vol 13 (6) ◽  
pp. 902-911 ◽  
Author(s):  
Yong Xue ◽  
Ziqiang Chen ◽  
Hui Xu ◽  
Jianwen Ai ◽  
Shuzheng Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Throughput
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