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R/UAStools::plotshpcreate:创建用于提取研究地块尺度农业遥感数据的多多边形 shapefile 文件。

R/UAStools::plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data.

作者信息

Anderson Steven L, Murray Seth C

机构信息

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States.

出版信息

Front Plant Sci. 2020 Sep 30;11:511768. doi: 10.3389/fpls.2020.511768. eCollection 2020.

DOI:10.3389/fpls.2020.511768
PMID:33101323
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7554333/
Abstract

Agricultural researchers are embracing remote sensing tools to phenotype and monitor agriculture crops. Specifically, large quantities of data are now being collected on small plot research studies using Unoccupied Aerial Systems (UAS, aka drones), ground systems, or other technologies but data processing and analysis lags behind. One major contributor to current data processing bottlenecks has been the lack of publicly available software tools tailored towards remote sensing of small plots and usability for researchers inexperienced in remote sensing. To address these needs we created plot shapefile maker (R/UAS::plotshpcreate): an open source R function which rapidly creates ESRI polygon shapefiles to the desired dimensions of individual agriculture research plots areas of interest and associates plot specific information. Plotshpcreate was developed to utilize inputs containing experimental design, field orientation, and plot dimensions for easily creating a multi-polygon shapefile of an entire small plot experiment. Output shapefiles are based on the user inputs geolocation of the research field ensuring accurate overlay of polygons often without manual user adjustment. The output shapefile is useful in GIS software to extract plot level data tracing back to the unique IDs of the experimental plots. Plotshpcreate is available on GitHub (https://github.com/andersst91/UAStools).

摘要

农业研究人员正在采用遥感工具来对农作物进行表型分析和监测。具体而言,目前正在使用无人机系统(UAS,即无人机)、地面系统或其他技术在小地块研究中收集大量数据,但数据处理和分析却滞后了。当前数据处理瓶颈的一个主要原因是缺乏专门针对小地块遥感且便于遥感经验不足的研究人员使用的公开可用软件工具。为满足这些需求,我们创建了地块形状文件制作工具(R/UAS::plotshpcreate):一个开源R函数,它能快速创建ESRI多边形形状文件,使其达到各个农业研究地块感兴趣区域的所需尺寸,并关联地块特定信息。开发plotshpcreate是为了利用包含实验设计、田间方位和地块尺寸的输入,以便轻松创建整个小地块实验的多多边形形状文件。输出的形状文件基于用户输入的研究田地理位置,确保多边形的准确叠加,通常无需用户手动调整。输出的形状文件在GIS软件中很有用,可用于提取可追溯到实验地块唯一ID的地块级数据。Plotshpcreate可在GitHub上获取(https://github.com/andersst91/UAStools)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/0fc987ec64cd/fpls-11-511768-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/0a048d53cdde/fpls-11-511768-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/e26ad96f571a/fpls-11-511768-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/0fc987ec64cd/fpls-11-511768-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/0a048d53cdde/fpls-11-511768-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/e26ad96f571a/fpls-11-511768-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2a7/7554333/0fc987ec64cd/fpls-11-511768-g003.jpg

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Translating High-Throughput Phenotyping into Genetic Gain.高通量表型分析转化为遗传增益。
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High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field.高通量表型分析以加速作物育种和田间病害监测。
Curr Opin Plant Biol. 2017 Aug;38:184-192. doi: 10.1016/j.pbi.2017.05.006. Epub 2017 Jul 21.
Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.
无人航空系统的时间表型预测可以优于基因组预测。
G3 (Bethesda). 2023 Jan 12;13(1). doi: 10.1093/g3journal/jkac294.
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AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.AirMeasurer:开源软件,可用于量化多季节航空表型衍生的静态和动态特征,为水稻遗传图谱研究提供支持。
New Phytol. 2022 Nov;236(4):1584-1604. doi: 10.1111/nph.18314. Epub 2022 Jul 28.
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Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.利用机器学习算法进行表型数据分析,预测玉米锈病和衰老。
Sci Rep. 2022 May 9;12(1):7571. doi: 10.1038/s41598-022-11591-0.