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Application trend of unmanned aerial vehicle (UAV) image in agricultural sector: Review and proposal

농업분야 무인항공기 영상 활용 동향: 리뷰 및 제안

  • Park, Jin-Ki (Dept. of Agr. and rural Eng., Chungbuk National University) ;
  • Das, Amrita (Dept. of Agr. and rural Eng., Chungbuk National University) ;
  • Park, Jong-Hwa (Dept. of Agr. and rural Eng., Chungbuk National University)
  • 박진기 (충북대학교 지역건설공학과) ;
  • ;
  • 박종화 (충북대학교 지역건설공학과)
  • Received : 2015.07.24
  • Accepted : 2015.09.17
  • Published : 2015.09.30

Abstract

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we discussed the state-of-the-art of the domestic and international use of UAV in agricultural sector as well as assessed its utilization and applicability for agricultural environment in Korea. Association of robotic, computer vision and geomatic technologies have established a new paradigm of low-altitude aerial remote sensing that has now been receiving attention from researchers all over the world. In a field study, it has been found that use of UAV imagery in an agricultural subsidy program can reduce the farmers' complain and provide objective evidence. UAV high resolution photography can also be helpful in monitoring the disposal zone for animal carcasses. Due to its expeditiousness and accuracy, UAV imagery can be a very useful tool to evaluate the damage in case of an agricultural disaster for both parties insurance companies and the farmers. Also high spatial and temporal resolution in UAV system can increase the prediction accuracy which in turn help to maintain the agricultural supply and demand chain.

Keywords

References

  1. Alderson, S. 2014. Drone cuts costs on New Zealand sheep farm. Farmers Weekly. Accessed in http://www.fwi.co.uk/machinery/drone-cuts-costs-on-new-zealand-sheep-farm.htm on 22 June 2015.
  2. Blank S. 2014. What drones and crop dusters can teach about minimum viable product. Harvard Business Review. Accessed in https://hbr.org/2014/02/what-drones-and-crop- dusters-can­teach-about-minimum-viable-product on 28 August 2015.
  3. CAK (Construction Association of Korea). 2015. Standard of construction estimate. [in Korean]
  4. EPIS (Korea Agency of Education, Promotion and Information Service in Food, Agriculture, Forestry and Fisheries). 2014. A Study on establishing application system to use eemote sensing technology in agrifood industry. [in Korean]
  5. Freeman PK, Freeland RS. 2014. Politics & technology: U.S. polices restricting unmanned aerial systems in agriculture. Food Policy. 49:302-311. https://doi.org/10.1016/j.foodpol.2014.09.008
  6. Freshplaza. 2014. More and more farmers using drones in France. Accessed in http:// www.freshplaza.com/article/118379/ More-and-more-farmers-using-drones-in-France on 22 June 2015.
  7. Gay AP, Stewart TP, Angel R, Easey M, Eves AJ, Thomas NJ, Pearce DA, Kemp AI. 2009. Developing unmanned aerial vehicles for local and flexible environmental and agricultural monitoring. Proceedings of RSPSoc 2009 Annual Conference. 8(11):471-476.
  8. Han SH. 2003. Landscape information acquisition and visu­alization techniquc in the rural landscape planning. The master's thesis, Chonnam National University. [in Korean]
  9. Herwitz SR, Dunagan S, Sullivan D, Higgins R, Johnson L, Zheng J, Aoyagi M. 2003. Solar-powered UAV mission for agricultural decision support. In International Geoscience and Remote Sensing Symposium 3:III-1692.
  10. KRC (Korea Rural Community Corporation). 2015. Development of the prediction and response technology for agricultural disasters based on ICT. [in Korean]
  11. KREI (Korea Rural Economic Institute). 2015. Introduce of business. Accessed in http://aglook.krei.re.kr on 22 September 2015. [in Korean]
  12. Na SI, Baek SC, Hong SY, Lee KD, Jang GC. 2015. A study on the application of UAV for the onion and garlic growth monitoring. KSSSF spring conference. [in Korean]
  13. Osborne C. 2012. Spy drones track European farms. Accessed in http://www.zdnet.com/article/spy-drones-track-european­farms on 28 August 2015.
  14. Park JK, Park JH. 2015. Reservoir failure monitoring and identified by the UAV aerial images. Korean Review of Crisis & Emergency Management, 11(4):156-167. [in Korean]
  15. Song YY. 2009. Design manufacturing test of fixed wing micro air vehicle with auto-pilot system. The master's thesis, Konkuk University. [in Korean]
  16. Teal Group, 2012. Worldwide UAS market will total $89 billion in 10 years. Accessed in http://tinyurl.com/l9hav8g on 22 June 2015.
  17. Waite R, Allan P. 2011. Rural Payments Agency (RPA) usage of Ordnance survey data. Accessed in http://www.slideshare.net/ CAPIGI/2b-rupert-amsterdam-lcd-v01 on 28 August 2015.

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