The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Analysis of Fusarium Wilt Based on Normalized Difference Vegetation Index for Radish Field Images from Unmanned Aerial Vehicle

무인기로 촬영한 무 재배지 영상의 정규식생지수(NDVI)를 활용한 병충해 분석 연구

  • Received : 2018.04.05
  • Accepted : 2018.09.27
  • Published : 2018.10.01
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Abstract

This paper compares and analyzes Fusarium wilt of radish by using an unmanned aerial vehicle(UAV) with the NDVI-7 camera. The UAV have taken near-infrared images of the Radish field in Gangwon area, which is affected by Fusarium wilt. Based on those images, we analyzed NDVI(Normalized difference vegetation index) and compared conditions of radish by using the Blue value among Regular Vegetation Index in NDVI. First, the radish field is divided into three fields for radish, soil and vinyl. Each field has separate Blue values that are radish 0.4890, soil 0.2959, vinyl -0.0605 respectively. Second, radish condition levels are divided into four stages which are normal, early, middle, and late stage of Fusarium wilt. The average values of each stage are normal 0.5165(100%), early 0.4565(88%), middle 0.3444(66%), and late 0.1772(34%) respectively. This result shows that this NDVI value is validated by measuring conditions of Radish and soil.

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References

  1. U. Amin, "A Survey on Precision Agriculture: Technologies and Challenges", The 3rd International Conference on Next Generation Computing (ICNGC2017b), December 21-24, 2017
  2. F. G. Costa, J. Ueyama, T. Braun, G. Pessin, F. S. Osorio and P. A. Vargas, "The use of unmanned aerial vehicles and wireless sensor network in agricultural applications", 2012 IEEE International Geoscience and Remote Sensing Symposium, July 22-27 2012.
  3. P. K. Freeman and R. S. Freeland, "Politics & technology: US polices restricting unmanned aerial systems in agriculture", Volume 49, Part 1, pp. 302-311, Dec 2014. https://doi.org/10.1016/j.foodpol.2014.09.008
  4. J. T. Kwak, J. G. Ha, S. H. Im, S. H. Shim, E. Kim, O. N. Lee, H, Y. Park, H. J. Moon, "Low-altitude Imagery of Unmanned Aerial Vehicles for Radish Monitoring: A Pilot Study", ICEIC 2017 International Conference on Electronics, pp. 933-934, Jan 2017.
  5. J. G. Ha, H. J. Moon, J. T. Kwak, I. H. Syed, L. M. Dang, O. N. Lee, H. Y. Park "Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles", Journal of Applied Remote Sensing, Vol. 11, No. 4, 042621, Dec 2017.
  6. P. C. Williams, K. Norris, Near-infrared technology in the agricultural and food industries, American Association of Cereal Chemists, 1987.
  7. Rouse Jr, W. John, "Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation", Texas A & M Univ, Nov, 1972.
  8. G. T. Yengoh, D. Dent, L. Olsson, A. E. Tengberg and C. J. Tucker, Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales, Springer, 2016.
  9. L. M. Dang, I. H. Syed, S. H. Im, M. Irfan, H. J. Moon, "Utilizing text recognition for the defects extraction in sewers CCTV inspection videos," Computers in Industry, Vol. 99, pp. 96-109, 2018 https://doi.org/10.1016/j.compind.2018.03.020