Advances, Limitations, and Future Applications of Aerospace and Geospatial Technologies for Apple IPM

사과 IPM을 위한 항공 및 지리정보 기술의 진보, 제한 및 미래 응용

  • Park, Yong-Lak (Entomology Program, Division of Plant and Soil Sciences, West Virginia University) ;
  • Cho, Jum Rae (Crop Protection Division, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Choi, Kyung-Hee (Research Policy Bureau, Rural Development Administration) ;
  • Kim, Hyun Ran (Crop Protection Division, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kim, Ji Won (Division of Agricultural Environment Research, Gyeongsangbuk-do Agricultural Research & Extension Services) ;
  • Kim, Se Jin (Floriculture Research Division, National Institute of Horticultural and Herbal Science, Rural Development Administration) ;
  • Lee, Dong-Hyuk (Apple Research Institute, National Institute of Horticulture and Herbal Science, Rural Development Administration) ;
  • Park, Chang-Gyu (Korea National College of Agriculture and Fisheries) ;
  • Cho, Young Sik (Apple Research Institute, National Institute of Horticulture and Herbal Science, Rural Development Administration)
  • 박용락 (웨스트 버지니아대학교) ;
  • 조점래 (국립농업과학원 작물보호과) ;
  • 최경희 (농촌진흥청 연구운영과) ;
  • 김현란 (국립농업과학원 작물보호과) ;
  • 김지원 (경상북도농업기술원 농업환경연구과) ;
  • 김세진 (국립원예특작과학원 화훼과) ;
  • 이동혁 (국립원예특작과학원 사과연구소) ;
  • 박창규 (한국농수산대학교) ;
  • 조영식 (국립원예특작과학원 사과연구소)
  • Received : 2021.01.31
  • Accepted : 2021.02.25
  • Published : 2021.03.01


Aerospace and geospatial technologies have become more accessible by researchers and agricultural practitioners, and these technologies can play a pivotal role in transforming current pest management practices in agriculture and forestry. During the past 20 years, technologies including satellites, manned and unmanned aircraft, spectral sensors, information systems, and autonomous field equipment, have been used to detect pests and apply control measures site-specifically. Despite the availability of aerospace and geospatial technologies, along with big-data-driven artificial intelligence, applications of such technologies to apple IPM have not been realized yet. Using a case study conducted at the Korea Apple Research Institute, this article discusses the advances and limitations of current aerospace and geospatial technologies that can be used for improving apple IPM.


  1. Bauer, M.E., 1985. Spectral inputs to crop identification and condition assessment. Proc. IEEE, 73, 1071-1085.
  2. Brewster, C.C., Allen, J.C., Kopp, D.D., 1999. IPM from space: Using satellite imagery to construct regional crop maps for studying crop-insect interaction. Am. Entomol., 45, 105-17.
  3. Carroll, M.W., Glaser, J.A., Hellmich, R.L., Hunt, T.E., Sappington, T.W., Calvin, D., Copenhaver, K., Fridgen, J., 2008. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots. J. Econ. Entomol., 101, 1614-1623.
  4. Chapman, R.F., 1999. The Insects: structure and function, 4th edition. Cambridge University Press, Cambridge.
  5. Clevers, J.G.P.W., 1999. The use of imaging spectrometry for agricultural applications. ISPRS J. Photogramm., 54, 299-304.
  6. Curran, P.J., 1985. Aerial photography for the assessment of crop condition: a review. Appl. Geogr., 5, 347-360.
  7. Felsot, A.S., Unsworth, J.B., Linders, J.B., Roberts, G., Rautman, D., Harris, C., Carazo, E., 2010. Agrochemical spray drift; assessment and mitigation - A review. J. Environ. Sci. Health Part B, 46, 1-23.
  8. Filho, F.H., Heldens, W.B., Kong, Z., de Lange, E.S., 2020. Drones: Innovative technology for use in precision pest management. J. Econ. Entomol., 113, 1-25.
  9. Fitzgerald, G.J., Maas, S.J., Detar, W.R., 2004. Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Prec. Agric., 5, 275-289.
  10. Hasan, R.I., Yusuf, S.M., Alzubaidi, L., 2020. Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion. Plants, 9, 1302.
  11. Hastie, T., Tibshirani, R., Friedman, J., 2009. Overview of supervised learning, in: Hastie, T., Tibshirani, R., Friedman, J. (Eds), The elements of statistical learning, Springer, New York, pp. 9-41.
  12. Heidary, M., Douzals, J.P., Sinfort, C., Vallet, A., 2014. Influence of spray characteristics on potential spray drift of field crop sprayers: a literature review. Crop Prot., 63, 120-130.
  13. Herren, H.R., Bird, T.J., Nadel, D.J., 1987. Technology for automated aerial release of natural enemies of the cassava mealybug and cassava green mite. Int. J. Trop. Insect Sci., 8, 883-885.
  14. Jensen, R.R., 1983. Biophysical remote sensing. Ann. Assoc. Am. Geogr., 73, 111-132.
  15. Kim, J., Huebner, C., Reardon, R., Park, Y.-L., 2021. Spatiallytargeted biological control of mile-a-minute weed using Rhinoncomimus latipes (Coleoptera: Curculionidae) and an unmanned aircraft system. J. Econ. Entomol., in press.
  16. Mogili, U.R., Deepak, B.B.V.L., 2018. Review on application of drone systems in precision agriculture. Proc. Comp. Sci., 133, 502-509.
  17. Moran, M.S., Inoue, Y., Barnes, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ., 61, 319-346.
  18. NGAC, 2016. Emerging technologies and the geospatial landscape. National Geospatial Advisory Committee, U.S. Department of Interior, Washington, D.C..
  19. Park, Y.-L., Gururajan, S. Thistle, H., Chandran, R., Reardon, R., 2018. Aerial release of Rhinoncomimus latipes (Coleoptera: Curculionidae) to control Persicaria perfoliata (Polygonaceae) using an unmanned aerial system. Pest Manag. Sci., 74, 141-148.
  20. Park, Y.-L., Krell, R.K., Carroll, M., 2007. Theory, technology, and practice of site-specific insect pest management. J. Asia-Pac. Entomol., 10, 89-101.
  21. Park, Y.-L., Tollefson, J.J., 2005. Spatial prediction of corn rootworm (Coleoptera: Chrysomelidae) adult emergence in Iowa cornfields. J. Econ. Entomol., 98, 121-128.
  22. Roosjen, P.P., Kellenberger, B., Kooistra, L., Green, D.R., Fahrentrapp, J., 2020. Deep learning for automated detection of Drosophila suzukii: potential for UAV‐based monitoring. Pest Manag. Sci., 76, 2994-3002.
  23. Rosenthal, G., 2017. PPQ explores the tantalizing promise of unmanned air- craft systems. USDA APHIS. from (accessed on 15 February, 2020).
  24. Russell, S., Norvig, P., 2020. Artificial Intelligence: A Modern Approach, 4th edition. Pearson, London.
  25. Rustia, D.J.A., Chao, J.J., Chiu, L.Y., Wu, Y.F., Chung, J.Y., Hsu, J.C., Lin, T.T., 2021. Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method. J. Appl. Entomol., in press.
  26. Taiz, L., Zeiger, E., 2006. Plant physiology, 4th edition. Sinauer Associates, Inc., Sunderland, MA.
  27. Wang, A., Zhang, W., Wei, X., 2019. A review on weed detection using ground-based machine vision and image processing techniques. Comp. Electron. Agric., 158, 226-240.
  28. Yang, Z., Rao, M.N., Elliott, N.C., Kindler, S.D., Popham, T.W., 2005. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Comp. Electron. Agric., 47, 121-135.
  29. Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K., Huang, W., 2019. Monitoring plant diseases and pests through remote sensing technology: a review. Comp. Electron. Agric., 165, 104943.