DOI QR코드

DOI QR Code

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 (Environment-friendly Agricultural Research Institute, JARES) ;
  • Ma, Kyung-Cheol (Environment-friendly Agricultural Research Institute, JARES) ;
  • Kim, Hyo-Jeong (Environment-friendly Agricultural Research Institute, JARES) ;
  • Lee, Jin-Hee (Environment-friendly Agricultural Research Institute, JARES) ;
  • Oh, Sang-A (Environment-friendly Agricultural Research Institute, JARES) ;
  • Kim, Seon-Gon (Environment-friendly Agricultural Research Institute, JARES)
  • 최덕수 (전남농업기술원 친환경농업연구소) ;
  • 마경철 (전남농업기술원 친환경농업연구소) ;
  • 김효정 (전남농업기술원 친환경농업연구소) ;
  • 이진희 (전남농업기술원 친환경농업연구소) ;
  • 오상아 (전남농업기술원 친환경농업연구소) ;
  • 김선곤 (전남농업기술원 친환경농업연구소)
  • Received : 2018.07.26
  • Accepted : 2018.11.22
  • Published : 2018.12.01

Abstract

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.

드론을 이용한 배추 해충 방제기준을 설정하기 위하여, 드론의 살포높이(3, 4, 5 m), 진행속도(3, 4 m/sec) 에 따른 하향풍 속도, 살포 폭, 그리고 낙하 입자수와 입자크기를 감수지를 이용하여 조사하였고, 항공방제용 농약 4종을 이용하여 배추 주요해충인 배추좀나방, 파밤나방, 담배거세미나방에 대하여 완전치사농도와 약량을 실험실에서 검정하였다. 드론의 농약 살포시 면별 낙하입자비율은 표면 80.5, 수직면 14.8, 밑면 4.7%였고, 살포높이에 따른 낙하입자수는 3 m = 53, 4 m = 40, 5 m = 39개/$cm^2$였다. 비행속도별 낙하입자수는 3 m/sec = 62, 4 m/sec = 25개/$cm^2$였다. 실내시험에서 배추좀나방의 완전치사농도와 치사량이 클로르페나피르액상수화제(20배, $0.5{\mu}l$) 비스트리플루론 클로르페나피르 액상수화제(25배, $0.5{\mu}l$)였다. 파밤나방에 대하여는 클로르페나피르액상수화제(20배, $1{\mu}l$), 비스트리플루론 클로르페나피르액상수화제(20배, $1{\mu}l$)였고, 담배거세미나방에 대하여는 클로르페나피르액상수화제(20배, $1{\mu}l$), 비스트리플루론 클로르페나피르액상수화제(20배, $0.5{\mu}l$)였다. 따라서 드론을 이용하여 배추 주요해충을 방제하는 방법으로 클로르페나피르액상수화제 또는 비스트리플루론 크로르페나피르액상수화제를 20배액으로 희석하여 3 m 높이에서 3 m/sec 속도로 살포하면 72개/$cm^2$의 농약입자가 낙하하므로 해충방제에 효과적일 것으로 판단된다.

Keywords

OOGCBV_2018_v57n4_347_f0001.png 이미지

Fig. 1. Tested drone and measurement equipment. A, Drone main body; B, Spray nozzle; C, Water sensitive paper (WSP) installation equipment; D, Spotted WSP.

Table 1. Downward wind speed and spray width at different spray heights by drone

OOGCBV_2018_v57n4_347_t0001.png 이미지

Table 2. Spot size on water sensitive paper when different agricultural chemical dosages are dropped

OOGCBV_2018_v57n4_347_t0002.png 이미지

Table 3. Number of spots dropped per cm2 according to the setting of the drone and the range of spot size

OOGCBV_2018_v57n4_347_t0003.png 이미지

Table 4. Spray characteristics of the agriculture chemical spray drone at different spray heights and speeds

OOGCBV_2018_v57n4_347_t0004.png 이미지

Table 5. Mortality (%) of Plutella xylostella third larva at 3 days after the treatment with agricultural chemicals

OOGCBV_2018_v57n4_347_t0005.png 이미지

Table 6. Mortality (%) of Spodoptera exigua third larva at 3 days after the treatment with agricultural chemicals

OOGCBV_2018_v57n4_347_t0006.png 이미지

Table 7. Mortality (%) of Spodoptera litura third larva at 3 days after the treatment with agricultural chemicals

OOGCBV_2018_v57n4_347_t0007.png 이미지

Table 8. Phytotoxicity of agrochemicals on Chinese cabbage (variety: Chu kwang)

OOGCBV_2018_v57n4_347_t0008.png 이미지

References

  1. Candiago, S., Remondino, F., Giglio, M.D., Dubbini, M., Gattelli, M., 2015. Evaluating multispectral selection of optimal vegetation indices for estimation of barley & wheat growth based on remote sensing -495- Images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7, 4026-4047. https://doi.org/10.3390/rs70404026
  2. Dieter, H., Werner, Z., Gunter, S., Peter, S., 2005. Monitoring of gas pipelines - a civil UAV application. Aircr. Eng. Aerosp. Technol. 77, 352-360. https://doi.org/10.1108/00022660510617077
  3. Feng, Q., Liu, J., Gong, J., 2015. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 7, 1074-1094. https://doi.org/10.3390/rs70101074
  4. Fuyi, T., Chun, B.B., Mat Jafri, M.Z., San, L.H., Abdullah, K., Tahrin, M., 2012. Land cover/use mapping using multi-band imageries captured by cropcam Unmanned Aerial Vehicle Autopilot (UAV) over Penang Island, Malaysia. Proc. of SPIE, 8540, 1-6.
  5. Hassan, F.M., Lim, H.S., Mat Jafri, M.Z., 2011. CropCam UAV for land use/land cover mapping over Penang Island, Malaysia. Pertanika J. Sci. Technol. 19, 69-76.
  6. Herwitz, S.R., John, L.F., Dunagan, S.E., Higgins, R.G., Sullivan, D.V., Zheng, J., Lobitz, B.M., Leung, J.G., Gallmeyer, B.A., Aoyagi, M., Slye, R.E., Brass, J.A., 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput. Electron. Agric. 44, 49-61. https://doi.org/10.1016/j.compag.2004.02.006
  7. Jee, S.H., Jeon, B.I., Cho, H.C., 2016. Implementation of aerial application system for application uniformity. J. Korea Acad. Industr. Coop. Soc. 17, 597-604.
  8. Jin, Y.D., Lee, H.D., Park, Y.K., Kim, J.B., Kwon, O.K., 2008. Drift and distribution properties of pesticide spray solution applied aerially by manned-helicopter. Korean J. Pestic. Sci. 12, 351-356.
  9. Jung, K.S., Kim, Y.S., Oh, S.R., 2015. Technical development of flood damage estimation using UAV. Mag. Korea Water Resour. Assoc. 51-59.
  10. Kang, T.G., Lee, C.S., Choi, D.K., Jun, H.J., Koo, Y.M., Kang, T.H., 2010. Development of aerial application system attachable to unmanned helicopter. Basic spraying characteristics for aerial application system. J. Biosyst. Eng. 35, 215-223. https://doi.org/10.5307/JBE.2010.35.4.215
  11. Kim, S.H., Lee, K.H., Ryu, K.H., 2018. Trends and Tasks of Agricultural Drone Technology. Institute of Control, Rovotics and Systems, pp. 34-42.
  12. Kim, S.M., Choi, H.J., Kim, H.Y., Lee, D.K., Kim, T.H., Ahn, M.S., 2002. Survey on pesticide use by Chinese cabbage grower in Gangwon alpine farmland. Korean J. Pestic. Sci. 6, 250-256.
  13. KOSIS., 2017. Survey of agriculture product : Amount of vegetable product (leaf vegetables). http://kosis.kr/ (accessed on 11 September, 2018).
  14. Lee, K.D., Na, S.I., Baek, S.C., Park, K.D., Choi, J.S., Kim, S.J., Kim, H.J., Yun, H.S., Hong, S.Y., 2015. Estimating the amount of nitrogen in hairy vetch on paddy fields using unmaned aerial vehicle imagery. J. Soil Sci. Fert. 48, 384-390.
  15. Lim, S.H., Song, B.H., 2009. Measuring the characteristic of aerial spray by rotary wing. J. Korean Soc. Aeronaut. Flight Oper. 17, 46-51.
  16. Na, S.I., Park, C.W., Cheong, Y.K., Kang, C.S., Choi, I.B., Lee, K.D., 2016. Selection of optimal vegetation indices for estimation of barley & wheat growth based on remote sensing - An application of unmanned aerial vehicle and field investigation data. Korean J. Remote Sens. 32, 483-497. https://doi.org/10.7780/kjrs.2016.32.5.7
  17. Park, J.H., Park, J.K., 2015. ICT-based agricultural disaster prediction/response technology development and pilot application - rice damage assessment prediction technology development. Ministry of Science, ICT and Future Planning, pp. 68-70.
  18. Park, J.K., Das, A., Park, J.H., 2015. Utilization trend of unmanned aerial vehicles in agriculture; Reviews and suggestions. Agricultural Science Research, pp. 269-276.
  19. Park, J.K., Park, J.H., 2015. Monitoring and identification of reservoir damage using UAV aerial image. Korea Crisis Management, pp. 156-167.