• Title/Summary/Keyword: 농업용 방제드론

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Control Standards of Three Major Insect Pests of Chinese Cabbage (Brassica campestris) Using Drones for Pesticide Application (농약살포용 드론을 이용한 배추 주요해충 3종의 방제기준 설정)

  • Choi, Duck-Soo;Ma, Kyung-Cheol;Kim, Hyo-Jeong;Lee, Jin-Hee;Oh, Sang-A;Kim, Seon-Gon
    • Korean journal of applied entomology
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    • v.57 no.4
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    • pp.347-354
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    • 2018
  • In order to setting the control standard of Chinese cabbage pests using a drone, the downward wind speed, spraying width, and the number of falling particles and particle size were examined using a water sensitive paper with spray different heights (3, 4, 5 m) and flying speeds (3, 4 m/sec). Fore kinds of pesticides for aviation control were used to test the perfect lethal concentration and dose for major pests of Chinese cabbage such as Plutella xylostella, Spodoptera exigua and Spodoptera litura. The number of falling particles in spraying pesticides with drones was 80.5% on the upper side, 14.8% on the vertical side, and 4.7% on the back side. The number of falling particles as different spray heights were 3 m = 53, 4 m = 40 and $5m=39particles\;cm^{-2}$. The number of falling particles as different flying speeds were $3m\;sec^{-1}=62$ and $4m\;sec^{-1}=25particles\;cm^{-2}$. In the laboratory test, the perfect lethal concentration and dose of Plutella xylostella was chlorfenapyr SC (20 times, $0.5{\mu}l$) and bistrifluron chlorfenapyr SC (25 times, $0.5{\mu}l$). The perfect lethal concentration and dose of Spodoptera exigua was chlorfenapyr SC (20 times, $1{\mu}l$), bistrifluron chlorfenapyr SC (20 times, $1{\mu}l$), and chlorfenapyr SC (20 times, $1{\mu}l$) and bistrifluron chlorfenapyr SC (20 times, $0.5{\mu}l$) for Spodoptera litura. Therefore, the main pest control method of Chinese cabbage using drones is 20 times diluted chlorphenapyr SC or bistrifluoruron-chlorphenapyr SC, sprayed at 3 m height by $3msec^{-1}$ of going speed. This spraying method will be effective for control of Chinese cabbage pest.

Susceptibility of Myzus persicae on Potato field and Riptortus clavatus on Soybean field to Insecticides treated by Multi-copter (농업용 멀티콥터를 활용한 감자의 복숭아혹진딧물과 콩의 톱다리개미허리노린재의 약제방제 효율)

  • Park, Bueyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.1
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    • pp.231-236
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    • 2021
  • The Aphid, Myzus persicae, and the bean bug, Riptortus clavatus, are major insects in crops. This study examined the insecticide susceptibility and phytotoxicity of insecticides dispersed using an Unmanned Aerial Vehicle (UAV, multi-copter) against the insects. Sulfoxaflor suspension concentrate (SC, 16X) on potato fields and etofenprox, methoxyfenzide suspo-emulsion(SE, 8X) on soybean fields were dispersed after deploying water-sensitive paper within the field to measure the distribution pattern and coverage index of the falling insecticide. Both insecticides showed a controlled mortality of 76.4% against aphids and 97.5% and 94.4% against the 2nd nymphal, and 5th nymphal stage of the bugs, respectively. The droplet distribution was less than 0.5mm, and coverage analysis revealed an inside and outside coverage of 3.1 and 1.6, respectively. The surrounding area was affected by insecticide spraying using a multi-copter. This study is expected to help expand UAV control and use it safely in the future.

Comparison of Each Commercial Nozzle on the Application Pattern of Pesticide for Unmanned Aerial Vehicles (UAV) (농업용 멀티콥터를 활용한 무인항공기용 작물보호제 살포양상에 대한 상용노즐별 차이)

  • Park, Bueyong;Jeong, In-Hong;Kim, Sun Woo;Kim, Gil-Hah
    • Korean journal of applied entomology
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    • v.60 no.2
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    • pp.229-234
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    • 2021
  • This study investigated spray patterns and coverage generated by three types of commercial nozzles for spraying pesticides with Unmanned Aerial Vehicles (UAVs) using a multi-copter. Flufenoxuron+metaflumizone SC and bifenthrin EC were sprayed. The falling particles of the spraying agent were measured using WSP (Water and oil Sensitive Paper) and the coverage was determined. The results showed that the uniformity of falling particles was different according to the difference in wind strength, and there was no difference for different formulations. The injection amount for each nozzle was found to be different from the official information provided by the manufacturers. These results could be used to establish guidelines for the control of UAVs and pesticide registration testing.

Predicting the spray uniformity of pest control drone using multi-layer perceptron (다층신경망을 이용한 드론 방제의 살포 균일도 예측)

  • Baek-gyeom Seong;Seung-woo Kang;Soo-hyun Cho;Xiongzhe Han;Seung-hwa Yu;Chun-gu Lee;Yeongho Kang;Dae-hyun Lee
    • Journal of Drive and Control
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    • v.20 no.3
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    • pp.25-34
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    • 2023
  • In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R2), the test samples showed an R2 of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

Density map estimation based on deep-learning for pest control drone optimization (드론 방제의 최적화를 위한 딥러닝 기반의 밀도맵 추정)

  • Baek-gyeom Seong;Xiongzhe Han;Seung-hwa Yu;Chun-gu Lee;Yeongho Kang;Hyun Ho Woo;Hunsuk Lee;Dae-Hyun Lee
    • Journal of Drive and Control
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    • v.21 no.2
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    • pp.53-64
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    • 2024
  • Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R2) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

Implementation of Agricultural Multi-UAV System with Distributed Swarm Control Algorithm into a Simulator (분산군집제어 알고리즘 기반 농업용 멀티 UAV 시스템의 시뮬레이터 구현)

  • Ju, Chanyoung;Park, Sungjun;Son, Hyoung Il
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 2017.04a
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    • pp.37-38
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    • 2017
  • 최근 방제 및 예찰과 같은 농작업에 단일 UAV(Unmanned Aerial Vehicle)시스템이 적용되고 있지만, 가반하중과 체공시간 등 기존시스템의 문제가 점차 대두되면서 작업 시간을 보다 단축시키고 작업 효율을 극대화 할 수 있는 농업용 멀티 UAV시스템의 필요성이 증대되고 있다. 본 논문에서는 작업자가 다수의 농업용 UAV를 효과적으로 제어할 수 있는 분산군집제어 알고리즘을 제안하며 알고리즘 검증 및 평가를 위한 시뮬레이터를 소개한다. 분산군집제어는 UAV 제어 계층, VP(Virtual Point) 제어 계층, 원격제어 계층으로 이루어진 3계층 제어구조를 가진다. UAV 제어 계층에서 각 UAV는 point mass로 모델링 되는 VP의 이상적인 경로를 추종하도록 제어한다. VP 제어 계층에서 각 VP는 입력 $p_i(t)=u^c_i+u^o_i+u^{co}_i+u^h_i$-(1)을 받아 제어되는데 여기서, $u^c_i{\in}{\mathbb{R}}^3$는 VP 사이의 충돌방지제어, $u^o_i{\in}{\mathbb{R}}^3$는 장애물과의 충돌방지제어, $u^{co}_i{\in}{\mathbb{R}}^3$는 UAV 상호간의 협조제어, $u^h_i{\in}{\mathbb{R}}^3$는 작업자로부터의 원격제어명령이다. (1)의 제어입력에서 충돌방지제어는 각 $u^i_c:=-{\sum\limits_{j{\in}{\eta}_i}}{\frac {{\partial}{\phi}_{ij}^c({\parallel}p_i-p_j{\parallel})^T}{{\partial}p_i}}$-(2), $u^o_c:=-{\sum\limits_{r{\in}O_i}}{\frac {{\partial}{\phi}_{ir}^o({\parallel}p_i-p^o_r{\parallel})^T}{{\partial}p_i}}$-(3)로 정의되면 ${\phi}^c_{ij}$${\phi}^o_{ir}$는 포텐셜 함수를 나타낸다. 원격제어 계층에서 작업자는 햅틱 인터페이스를 통해 VP의 속도를 제어하게 된다. 이때 스케일변수 ${\lambda}$에 대하여 VP의 원격제어명령은 $u^t_i(t)={\lambda}q(t)$로 정의한다. UAV 시뮬레이터는 리눅스 환경에서 ROS(Robot Operating Systems)를 기반한 3차원 시뮬레이터인 Gazebo상에 구축하였으며, 마스터와 슬레이브 간의 제어 명령은 TCPROS를 통해 서로 주고받는다. UAV는 PX4 기반의 3DR Solo 모델을 사용하였으며 MAVROS를 통해 MAVLink 통신 프로토콜에 접속하여 UAV의 고도, 속도 및 가속도 등의 상태정보를 받을 수 있다. 현재 멀티 드론 시스템을 Gazebo 환경에 구축하였으며, 추후 시뮬레이터 상에 분산군집제어 알고리즘을 구현하여 검증 및 평가를 진행하고자 한다.

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