KOREAN JOURNAL OF CROP SCIENCE (한국작물학회지)

The Korean Society of Crop Science (한국작물학회)
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Case Study: Cost-effective Weed Patch Detection by Multi-Spectral Camera Mounted on Unmanned Aerial Vehicle in the Buckwheat Field

  • Kim, Dong-Wook (Department of Biosystems & Biomaterials Science and Engineering, College of Agriculture and Life Sciences, Seoul National University) ;
  • Kim, Yoonha (Plant Bioscience, School of Applied Biosciences, Kyungpook National University) ;
  • Kim, Kyung-Hwan (National Institute of Agricultural Sciences, Rural Development Administration (RDA)) ;
  • Kim, Hak-Jin (Department of Biosystems & Biomaterials Science and Engineering, College of Agriculture and Life Sciences, Seoul National University) ;
  • Chung, Yong Suk (Department of Plant Resources and Environment, Jeju National University)
  • Received : 2019.05.01
  • Accepted : 2019.06.08
  • Published : 2019.06.30
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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

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Fig. 2. Flow chart of the image processing and analysis steps.

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Fig. 3. Generated normalized difference vegetation index (NDVI) map of the whole test site (upper) and detected weed map based on NDVI value (lower). NDVI value reflects chlorophyll content.

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Fig. 4. Normalized difference vegetation index (NDVI) histogram for weed detection with threshold value based on inflection point.

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Fig. 5. Normalized difference vegetation index (NDVI) histogram for weed detection with a threshold value based on inflection point.

Table 1. Specifications of developed unmanned aerial vehicle (UAV) platform.

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Fig. 1. (a) View of a commercial multi-spectral camera with sunshine sensor (Parrot Sequoia) and (b) spectral response of four bands in the camera.

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Table 2. Technical specifications of multi-spectral camera.

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Table 3. Experimental unmanned aerial vehicle (UAV) flight details.

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Table 4. Number of grids extracted from the generated grid map.

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