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2019 Vol.64, Issue 2 Preview Page

Original Research Article

Case Study: Cost-effective Weed Patch Detection by Multi-Spectral Camera Mounted on Unmanned Aerial Vehicle in the Buckwheat Field
Dong-Wook Kim1*
,
Yoonha Kim2*
,
Kyung-Hwan Kim3
,
Hak-Jin Kim1**
,
Yong Suk Chung4**
1Department of Biosystems & Biomaterials Science and Engineering, College of Agriculture and Life Sciences, Seoul National University
2Plant Bioscience, School of Applied Biosciences, Kyungpook National University
3National Institute of Agricultural Sciences, Rural Development Administration (RDA)
4Department of Plant Resources and Environment, Jeju National University
*These authors also contributed equally to this work.
**Corresponding Author
30 June 2019. pp. 159-164
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Abstract

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

Keywords
normalized difference vegetation index (NDVI)
remote sensing
site-specific weed management (SSWM)
weed distribution
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Information
  • Publisher :The Korean Society of Crop Science
  • Publisher(Ko) :한국작물학회
  • Journal Title :The Korean Journal of Crop Science
  • Journal Title(Ko) :한국작물학회지
  • Volume : 64
  • No :2
  • Pages :159-164
  • Received Date : 2019-05-01
  • Revised Date : 2019-06-07
  • Accepted Date : 2019-06-08
  • DOI :https://doi.org/10.7740/kjcs.2019.64.2.159
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