• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.375-387
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• 2019

The use of drone-based hyperspectral image offers considerable advantages in high resolution remote sensing applications. The primary objective of this study was to select the optimal bands based on hyperspectral image for the estimation yield of the chinese cabbage. The hyperspectral narrow bands were acquired over 403.36 to 995.19 nm using a 3.97 nm wide, 150 bands, drone-based hyperspectral imaging sensor. Fresh weight data were obtained from 2,031 sample for each field survey. Normalized difference vegetation indices were computed using red, red-edge and near-infrared bands and their relationship with quantitative each fresh weights were established and compared. As a result, predominant proportion of fresh weights are best estimated using data from three narrow bands, in order of importance, centered around 697.29 nm (red band), 717.15 nm (red-edge band) and 808.51 nm (near-infrared band). The study determined three spectral bands that provide optimal chinese cabbage productivity in the visible and near-infrared portion of the spectrum.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.4
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• pp.377-385
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• 2020

Recently, wheat consumption has been increasing in Korea, requiring increased production. Nitrogen fertilization is a critical determinant in crop yield; therefore, it is necessary to optimize the nitrogen fertilization regime with current trends that emphasize the minimum impact of nitrogen fertilizer on the environment. In this study, both nondestructive spectral analysis using a hyperspectral camera and growth analysis were performed to determine the optimal N top-dressing rates after heading. The nitrogen application regimes consisted of three conditions according to the secondary top-dressing rate: N_4:3:0 (0 kg 10 a^-1), N_4:3:3 (2.73 kg 10 a^-1), and N_4:3:6 (5.46 kg 10 a^-1). Subsequently, growth and physiological investigations were performed at the jointing, heading, and ripening stages of wheat, and spectral investigations were conducted. On April 29, as the nitrogen fertilization rate was increased to N_4:3:3 and N_4:3:6, plant height and grain yield increased by 4% and 8%, and 8% and 52%, respectively, compared to those under N_4:3:0. Leaf area index and SPAD value also increased by 13% and 24%, and 32% and 43%, respectively. The R (red), G (green), and B (blue) of leaf color were lowered by 15, 11, and 4 in N_4:3:3 and 44, 34, and 18 in N_4:3:6, respectively, as compared to the control. Grain yield was the highest at high top-dressing (N_4:3:6), however, there was no difference between no top-dressing (N_4:3:0) and intermediat top-dressing (N_4:3:3). The reflectance analyzed using a hyperspectral camera showed a difference in the near-infrared (NIR) region on March 19, and on April 29, there was a difference both in the visible light region greater than 550 nm and the NIR region. Vegetation indices differed according to fertilization regime, except for the greenness index (GI). The results of this study showed that not only growth and physiological analysis but also spectral indices can be used to optimize the nitrogen top-dressing rate.