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Indoor and Outdoor Tests for a Chemi-capacitance Carbon Nanotube Sensor Installed on a Quadrotor Unmanned Aerial Vehicle for Dimethyl Methylphosphonate Detection and Mapping.

ABSTRACT: Unmanned aerial vehicles (UAVs) have been used as a new chemical reconnaissance platform in chemical, biological, radiological, and nuclear detection and in industrial monitoring and environmental research, owing to their mobility, unconventional accessibility, and safety. Based on the UAV's payload and operational time considerations, the ultralight chip-sized chemical sensor is the most promising candidate for chemical reconnaissance among various chemical sensors. To optimize the UAV's chip-sensor performance, realistic outdoor tests of chemical sensors during UAV flights have to be conducted to verify their performances. In this study, indoor and outdoor experiments were conducted with a carbon nanotube (CNT)-based chip sensor installed on the UAV to detect dimethyl methylphosphonates (DMMPs), commonly used as chemical warfare agent simulants. Based on the indoor tests, DMMP concentrations and the position/direction of the CNT sensor were analyzed to optimize the sensing performances during UAV operations. Based on outdoor tests, we confirmed that the chemical sensor mounted on the UAV could detect DMMP gases by moving designated pathways in realistic conditions.

SUBMITTER: Kim JS

PROVIDER: S-EPMC8223397 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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

Indoor and Outdoor Tests for a Chemi-capacitance Carbon Nanotube Sensor Installed on a Quadrotor Unmanned Aerial Vehicle for Dimethyl Methylphosphonate Detection and Mapping.

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