The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV's acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer's output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.
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http://dx.doi.org/10.3390/s22114021 | DOI Listing |
J Agric Food Chem
January 2025
Joint Research Center for Food Nutrition and Health of IHM, School of Plant Protection, Anhui Agricultural University, Hefei, Anhui 230036, China.
The use of unmanned aerial vehicle (UAV) has greatly improved pesticide effectiveness and control efficiency; however, the risk of inhalation exposure to pesticides caused by spray drift requires urgent attention. This study is the first to investigate residue distribution and inhalation exposure risk of airborne prothioconazole and its metabolite prothioconazole-desthio during UAV application. The maximum detected unit exposure of prothioconazole and prothioconazole-desthio in airborne particulate matter was 0.
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January 2025
College of Computer and Control Engineering, Northeast Forestry University, Haerbin, 150040, Heilongjiang, China.
Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To address this challenge, we propose SC-RTDETR, a novel framework for secure and robust object detection in forest pest monitoring using UAV imagery.
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January 2025
Postdoctoral Innovation Practice Base, Chengdu Textile College, Chengdu, 611731, China.
In radar systems, element gain-phase errors can degrade the performance of space-time adaptive processing (STAP), and even cause complete failure. To address this issue, the STAP with the coprime sampling structure based on optimal singular value thresholding is proposed. The algorithm corrects errors by adding four calibrated auxiliary elements and auxiliary pulses to the original array and pulse sequence, while maintaining the coprime sampling structure.
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December 2024
Limagrain Brazil S.A., Jataí, Goiás, Brazil.
This study investigates the effectiveness of high-throughput phenotyping (HTP) using RGB images from unmanned aerial vehicles (UAVs) to assess vegetation indices (VIs) in different soybean pure lines. The VIs were accessed at various stages of crop development and correlated with agronomic performance traits. The field research was conducted in the experimental area of the Mato Grosso do Sul Foundation, Brazil, with 60 soybean pure lines.
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December 2024
Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan.
Legume content (LC) in grass-legume mixtures is important for assessing forage quality and optimizing fertilizer application in meadow fields. This study focuses on differences in LC measurements obtained from unmanned aerial vehicle (UAV) images and ground surveys based on dry matter assessments in seven meadow fields in Hokkaido, Japan. We propose a UAV-based LC (LC) estimation and mapping method using a land cover map from a simple linear iterative clustering (SLIC) algorithm and a random forest (RF) classifier.
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