• Preventive Nutrition and Food Science
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• v.15 no.3
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• pp.221-228
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• 2010

The objective of this research was to investigate the changes in the contents of physicochemical properties including $\gamma$-aminobutyric acid (GABA), total dietary fiber (TDF), amylose, protein, and fat content in brown rice through germination for 2 different years. Total phenolic contents and antioxidant activities of DPPH and ABTS radical scavenging capacities were also determined in different solvent extracts. For the physicochemical properties, GABA, TDF, protein, and fat content increased, whereas amylose levels decreased. Specially, GABA and TDF levels showed the greatest variations among cultivars and harvest years. Total phenolic content and antioxidant activity significantly increased. The average total phenolic content at a concentration of 0.5 mg/mL in different extract solvents occurred in this order: methanol>ethylacetate>chloroform>hexane extracts. Additionally, 'Keunnun' exhibited the highest GABA levels, highest total phenolic content, and highest antioxidant activity after germination, with increases of approximately 3.7, 2.0, and 1.9 times, respectively, compared to levels before germination. These results suggest that, because of its high physicochemical contents and strong radical scavenging activities, germinated brown rice can be used as beneficial supplement.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.993-1001
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• 2017

Drought is a worldwide natural disaster with extensively adverse impacts on natural ecosystems, agricultural products, social communities and regional economy. Various global satellite observations, including SMAP soil moisture, GRACE terrestrial water storage, Terra and Aqua vegetation productivity, evapotranspiration, and satellite precipitation measures are currently used to characterize seasonal timing and inter-annual variations of regional water supply pattern, vegetation growth, drought events, and its associated influence ecosystems and human society. We suggest the satellite monitoring system development to quantify meteorological, eco-hydrological, and socio-ecological factors related to drought events, and characterize spatial and temporal drought patterns in Korea. The combination of these complementary remote sensing observations(visible to microwave bands) provide an effective means for evaluating regional variations in the timing, frequency, and duration of drought, and availability of water supply influencing vegetation and crop growth. This integrated drought monitoring could help national capacity to deal with natural disasters.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.109-121
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• 2020

Converting the agricultural land-use of rice field to upland has been increasingly conducted as farmers encourages themselves to grow higher value-added crops on rice fields under the policy support. Comparing to rice field, Upland shows different characteristic of discharge due to the slope, scale, and shape of field and characteristics of rainfall event. In this study, we designed the experiment fields reflecting flat-upland characteristics with different land scale, and tried to collect the discharge and load data. Soybeans and corn were selected as target crops considering the possibility of large-scale cultivation and crop demand. The cultivation was conducted during the growth period in 2019 with 3 different field scales. Hence, we have collected the discharge data from 17 rainfall events and the load data for 8 rainfall events. As a result, the magnitude of rainfall events and the discharge duration were found to have a strong positive correlation and field discharge occurred during the period by 55% to 83% of rainfall duration. Besides we found other relationships and characteristics of rainfall event, discharge, and pollutant load and also pointed out that continuous monitoring and more data are required to derive statistically significant results. Compared with slope-field monitoring data obtained from the precedent research, the runoff ratio of the flat-fields was significantly lower than slope-fields. Overall the discharge in the slop and flat-fields shows appreciably different characteristics so that the related researches need to be further conducted to reasonably assess environmental impact of agricultural activities at flat-field.

• Journal of Forest and Environmental Science
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• v.38 no.2
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• pp.128-132
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• 2022

Wild boars (Sus scrofa) not only cause crop damage and human casualties, but also facilitate the spread of many infectious diseases in domestic animals and humans. To determine the efficiency of a fencing system in blocking the movement of wild boars, long-term monitoring was performed in a fenced area in Bukhansan National Park using camera traps. Upon monitoring for a period of 46 months, there was a 72.6% reduction in the number of wild boar appearances in the fence-enclosed area, compared to that in the unenclosed area. For 20 months after the fence installation, the blocking effect of the fence was effective enough to reduce the appearance of wild boars by 92.6% in the fence-enclosed area, compared to that in the unenclosed area. The blocking effect of the fence remained effective for 20 months after its installation, after which its effectiveness decreased. Maintaining a fence for a long time is likely to lead to habitat fragmentation. It can also block the movement of other wild animals, including the endangered species - the long-tailed goral. This study suggests a 20-month retention period for the fences installed to inhibit the movement of wild boars in wide forests such as Gangwon-do in South Korea. To identify how long the blocking effect of the fences lasts, further studies are needed focusing on the length and height of the fence, and the conditions of the ground surface.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.173-184
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• 2019

BACKGROUND: This study was carried out to investigate pesticide residues from fifty streams in Korea. Water samples were collected at two times. Thee first sampling was performed from april to may, which was the season for start of pesticide application and the second sampling event was from august to september, which was a period for spraying pesticides multiple times. METHODS AND RESULTS: The 136 pesticide residues were analyzed by LC-MS/MS and GC/ECD. As a result, eleven of the pesticide residues were detected at the first sampling. Twenty eight of the pesticide residues were detected at the second sampling. Seven pesticides were frequently detected from more than 10 water samples. Ecological risk assessment (ERA) was carried out by using residual and toxicological data. Four scenarios were applied for the ERA. Scenario 1 and 2 were performed using LC50 values and mean and maximum concentrations. Scenarios 3 and 4 were conducted by NOEC values and mean and maximum concentrations. CONCLUSION: Frequently detected pesticide residues tended to coincide with the period of preventing pathogen and pest at paddy rice. As a result of ERA, five pesticides (butachlor, carbendazim, carbofuran, chlorantranilprole, and oxadiazon) were assessed to be risks at scenario 4. However, only oxadiazon was assessed to be a risk at scenario 3 for the first sampling. Oxadiazon was not assessed to be a risk at the second sampling. It seems to be temporary phenomenon at the first sampling, because usage of herbicides such as oxadiazon increased from April to march for preventing weeds at paddy fields. However, this study suggested that five pesticides which were assessed to be risks need to be monitored continuously for the residues.

• Korean Journal of Environmental Agriculture
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• v.38 no.4
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• pp.273-280
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• 2019

BACKGROUND: The reclaimed lands for agricultural use managed by the Korean government is consisted of 17,145 hectares of lands under construction and 13,384 hectares of completed lands. In order to utilize these reclaimed lands as competitive agricultural complexes, the government is preparing to develop comprehensive development plans for multiple purposes. For rational land-use planning and soil management, information of the soil chemical properties is necessary. METHODS AND RESULTS: From 2013 to 2016, soil samples were collected from 85 representative sampling sites of the reclaimed lands and analyzed for soil chemical properties including electric conductivity (EC), pH, soil organic matter (SOM), and nutrients. The annual mean soil EC ranged from 5.1 to 8.3 dS m^-1 and have continued to decrease over the years (estimation equation with EC as dependent and year as independent variable was y =0.0736x² - 1.4985x + 9.8305, R² = 0.9753). The pH ranged from 7.3 to 7.6, which was higher than the optimum range (5.5~7.0) for agricultural soils. Soil organic matter (8 to 11 g kg^-1) was lower level than the optimum range (20~30 kg^-1). Available silicate (Av.SiO₂) ranged from 169 to 229 mg kg^-1, which was close to the minimum content (≥157 mg kg^-1) for rice paddy field. Available phosphate (Av.P₂O₅) content (24~39 mg kg^-1) was lower than the optimum range (80~120 mg kg^-1) for rice paddy field. CONCLUSION: For efficient agricultural use of reclaimed lands under government management, our results suggest that the application of organic matter and supplying deficient nutrients as well as desalinization is required.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.