Dataset Information

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory.

ABSTRACT: We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system's own original gas using its pump. CO2 and PM2.5/10 above the corn field are used as the test species to validate the accuracy of the CO2 gas and PM concentrations collected by UGSS. Deming regression analyses showed good agreement between the measurements from the UGSS and the ground sampling station (y = 1.027x - 11.239, Pearson's correlation coefficient of 0.98 for CO2; y = 0.992x + 0.704, Pearson's correlation coefficient of 0.99 for PM).The UGSS provides a measuring method that actively collects gases and PM for manual analyses in the laboratory.

SUBMITTER: Li C

PROVIDER: S-EPMC7070813 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO<sub>2</sub> and Atmospheric Particulate Matter in Laboratory.

Li Chaoqun C Han Wenting W Peng Manman M Zhang Mengfei M Yao Xiaomin X Liu Wenshuai W Wang Tonghua T

Sensors (Basel, Switzerland) 20200215 4

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. ...[more]

PMID: 32075222

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Project description:BackgroundThe technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.ResultsIn this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.ConclusionsDense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Dataset Information

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory.

Publications

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO<sub>2</sub> and Atmospheric Particulate Matter in Laboratory.

Similar Datasets

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