• The Korean Journal of Pesticide Science
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• v.18 no.4
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• pp.350-357
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• 2014

We studied on pesticide residue and pest control effect when using various types of sprayers on paprika cultivation. Additionally, a test was conducted to optimize chemical content per unit area in condition of optimum pest control. Two types of sprayer were tested (three times) on paprika cultivation which was divided into seven sections. Blind spots were also examined using a water sensitive paper when spraying chemical pesticide, remote controlled sprayer were confirmed to be not effective in terms of its spraying capacity. However, a U-shaped sprayer was confirmed that it sprayed enough on all the parts of a plant in green house including the blind spots. Additionally, it does not exceed the minimum residue limits on the all parts of pesticides residue conditions. When using remote controlled sprayer, water sensitive paper were changed to blue color (82.5% and 81.2%) in terms of controlling Bemisia tabaci and Aphis gossypii based on the two spraying manners. 53.0% and 42.6% of control effect were shown on the fair parts of the plants. However, on the poor parts on which pesticides were not well-sprayed, thus, not-remained, more number of pests increased. Meanwhile, on farming that only one type of pesticide has been used, resistance pests present with very low control effect, even though sufficient amount of pesticide was well-sprayed. On the test of the optimum amount of spraying per a unit area, which shows no differences in the two cases of using 5L and 2.5L of chemical pesticides on 9 plants of paprika that has 81.8% and 84.5% control effect, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.3
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• pp.162-178
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• 2017

Remote sensing can be used to provide information about the monitoring of crop growth condition. Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to assess weather UAV aerial images are suitable for the monitoring of highland Kimchi cabbage. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110, IXUS/ELPH camera during farming season from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. The Normalized Difference Vegetation Index (NDVI) by using UAV images was stable and suitable for monitoring of Kimchi cabbage situation. There were strong relationships between UAV NDVI and the growth parameters (the plant height and leaf width) ($R^2{\geq}0.94$). The tendency of UAV NDVI according to Kimchi cabbage growth was similar in the same area for two years (2015~2016). It means that if UAV image may be collected several years, UAV images could be used for estimation of the stage of growth and situation of Kimchi cabbage cultivation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.1-8
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• 2020

In this paper, we proposed a smart greenhouse system that can easily grow plants indoors without professional knowledge by using the criteria of factors affected by common plants (temperature, humidity, soil humidity), and implemented a system that can check the greenhouse state in real time and control the device remotely through mobile applications. Based on Raspberry pie and Arduino, the system measures the state of greenhouse in real time through sensors and automatically controls the device. After growing and experimenting with plants in a greenhouse for a certain period of time, it was confirmed that the environment suitable for each plant was maintained. Therefore, the smart greenhouse system in this paper is expected to improve plant cultivation efficiency and user convenience and also increase beginners' access to plants.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of Practical Agriculture & Fisheries Research
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• v.10 no.1
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• pp.43-58
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• 2008

This research was conducted to determine the alternative tool of chemical spray for rice cultivation using the unmanned helicopter(Yamaha, R-Max Type 2G-remote controlled system) at farmer's field in Korea. The unmanned helicopter tested was introduced form Japan. In Korea the application of chemicals by machine sprayer for pest management in rice cultivation has been ordinarily used at the farmer's level. However, it involved a relatively high cost and laborious for the small scale of cultivation per farm household. Farm population has been highly decreased to 7.5% in 2002 and the population is expected to rapidly reduce by 3.5% in 2012. In Japan, pest control depending on unmanned helicopter has been increased by leaps and bounds. This was due in part to the materialization of the low-cost production technology under agricultural policy and demand environmentally friendly farm products. The practicability of the unmanned helicopter in terms of super efficiency and effectiveness has been proven, and the farmers have understood that the unmanned helicopter is indispensable in the future farming system that they visualized. Also, the unmanned helicopter has been applied to rice, wheat, soybean, vegetables, fruit trees, pine trees for spraying chemicals and/or fertilizers in Japan Effect of disease control by unmanned helicopter was partially approved against rice blast and sheath blight. However, the result was not satisfactory due to the weather conditions and cultural practices. The spray density was also determined in this experiment at 0, 15, 30, and 60cm height from the paddy soil surface and there was 968 spots at 0cm, 1,560 spots at 15cm, 1,923 spots at 30cm, and 2,999 spots at 60cm height. However, no significant difference was found among the treatments. At the same time, there was no phytotoxicity observed under the chemical stray using this unmanned helicopter, nor the rice plant itself was damaged by the wind during the operation.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.