| Summary: | Kimchi cabbage grows in South Korea and is an essential ingredient for making kimchi with the kimjang method. The technique of accurately managing and monitoring crops such as kimchi cabbage plays an important role in stabilizing consumer prices. Unmanned aerial vehicles (UAVs) are expected to be used more widely in global and local agriculture. The agricultural sites at which kimchi cabbages are cultivated are affected by various climatic, terrain, and soil conditions, requiring technologies that can accurately and quickly acquire such information. UAVs and sensors are able to provide some of these data. In this study, we set up a cultivation environment for kimchi cabbage and investigated the correlation between a UAV-attached multispectral sensor and a field-portable spectroradiometer. Reflectance measurement using a spectroradiometer was performed on 99 kimchi cabbages in a Mt. Maebong testbed. We aimed to find a method for obtaining accurate vegetation information by combining the high spatial and temporal resolution information of the UAV observation with the spectral resolution of the spectroradiometer. Spectral analysis was used to identify the difference between healthy and poorly growing cabbage and to find the wavelength that most affected the growth. The hyperspectrum of the spectroradiometer reflected the accurate vegetation characteristics and contributed greatly to the identification of vegetation indices. A method for correcting the errors that occurred in the ground and UAV monitoring and the difference arising from the application of the broadband wavelength of the UAV and the single wavelength of the spectroradiometer through correlation analysis is presented. The calibration equation method was applied to UAV spatial information and was used to create a precise normalized distribution vegetation index (p-NDVI) map. The p-NDVI map was organized into four categories for the selection of cabbages with healthy (good) growth. Our results show that (1) the merged spectral analysis method was found to be more accurate and distinct than conventional methods, and (2) methods for estimating cabbage growth status showed a higher significant correlation than the UAV-based NDVI. At the maturity stage, high accuracy (R<sup>2</sup> = 0.7816, RMSE = 0.06) was achieved for NDVI. Although this map is the result of the limited vegetation monitoring of UAV images taken during the maturity stage, it could be of great help for managing the quality and production of cabbage. However, the efficient management of highland kimchi cabbage requires continuous research under various conditions to enable periodic and systematic monitoring using UAVs and sensors.
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