• The Korean Journal of Food & Health Convergence
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• v.4 no.3
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• pp.6-11
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• 2018

In the Republic of Belarus as well as in the world acute problem of protecting forests from diseases and pests. The damage caused by root rot is essential, therefore, the problem of forest protection is an urgent task. The biologics has the greatest prospects in according with traditional methods of struggle. Deep method of cultivation of a mushroom Phlebiopsis gigantea with use of nutrient mediums on the basis of ethanol stillage and its components (fugat) is researched. Feasibility of use stillage as raw materials in production of a biological product for the wood protection against root decay is shown. The effect of different additives (sawdust, fodder yeast) on the accumulation of reactive biological product - oidy has been studed It was determined that the deep cultivation using sawdust of the highest accumulation oidy (1.5 $10^6units/ml$). It was also found that the stillage is the best breeding ground for fungus biomass accumulation (7.9 9.8 g / l) versus fugat (6.0 6.6 g / l). On the basis of research work the technological scheme for production of a biological product were developed. Based on the conducted studies, a technological scheme was proposed for obtaining a biological preparation by deep cultivation of the fungus Phlebiopsis gigantea.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• 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.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• ALGAE
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• v.18 no.2
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• pp.129-134
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• 2003

In order to examine the effects of deep seawater (mesopelagic water in the broad sense) on the growth of macroalgae, the growth and nutrient uptake (nitrate and phosphate) of Ulva sp. (Ulvophyceae, Chlorophyta) were investigated by cultivation in deep seawater (taken from 687 m depth at Yaizu, central Japan, in August 2001), surface seawater (taken from 24 m depth), and a combination of the two. Culture experiments were carried out in a continuous water supply system and an intermittent water supply system, in which aerated 500-mL flasks with 4 discs of Ulva sp. (cut sections of ca. 2 $cm_2$) were cultured at 20$^{\circ}C$ water temperature, 100 $\mu$mol photons $m^{-2}{\cdot}s^{-1}$ light intensity, and a 14:10 light:dark cycle. Nutrient uptake by Ulva sp. was high in all seawater media in both culture systems. The frond area, dry weight, chlorophyll a content, dry weight per unit area, and chlorophyll a content per unit area of Ulva sp. at the end of the experimental period were the highest in deep seawater and the lowest in surface seawater in both culture systems. These values, except for dry weight per unit area and chlorophyll a content per unit area, for each seawater media in the intermittent water supply system were higher than those in the continuous water supply system. We conclude that not only deep seawater as the culture medium but also the seawater supply system is important for effective cultivation of macroalgae.

• Korean journal of food and cookery science
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• v.17 no.5
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• pp.510-516
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• 2001

Hydroponic cultivation is a technology of raising crops without use of soil. Generally farmers use the method of DFT(deep flow technology)to grow leafy or fruity vegetables; however, systematic and scientific researches are insufficient on this matter. This study investigated the possibility of cultivating Chinese cabbage steadily year long by using the method of DFT. Chinese cabbage was cultivated hydroponically with and without Ge addition, used to prepare kimchi, and the chemical and microbiological characteristics of kimchi were compared. The basic hydroponic cultivation condition was as follows: 30 days after seeding, the raised seeds were moved to a hydroponic bed and given underground water for 3 days so the roots grow normally Standard nutrient solution was provided and the early electric conductivity concentration was maintained between 1.5∼2.5 thickness. The temperature of the solution was maintained between 10 ∼25$^{\circ}C$ to allow the growth of Chinese cabbage. When soil-cultivated, organically cultivated and hydroponically cultivated Chinese cabbages were compared, hydroponically cultivated cabbages were smaller in size and showed less ability to build up and fold leaves into a head, but showed better quality than organically cultivated cabbages. The contents of protein and fat showed no significant differences. The contents of water. Ca, P, Fe, Vitamin A and Niacin were higher in control and Ge-added cabbages compared with soil-grown cabbage. There was no difference between soil-cultivated Chinese cabbage kimchi and hydroponically cultivated Chinese cabbage kimchi.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.253-265
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• 2021

In this study, crop cultivation filed was extracted by using Unmanned Aerial Vehicle (UAV) imagery and deep learning models to overcome the limitations of satellite imagery and to contribute to the technological development of understanding the status of crop cultivation field. The study area was set around Chungbuk Goesan-gun Gammul-myeon Yidam-li and orthogonal images of the area were acquired by using UAV images. In addition, study data for deep learning models was collected by using Farm Map that modified by fieldwork. The Attention U-Net was used as a deep learning model to extract feature of UAV in this study. After the model learning process, the performance evaluation of the model for corn cultivation extraction was performed using non-learning data. We present the model's performance using precision, recall, and F1-score; the metrics show 0.94, 0.96, and 0.92, respectively. This study proved that the method is an effective methodology of extracting corn cultivation field, also presented the potential applicability for other crops.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.329-332
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• 2003

Aquaculture have been important role to supply food resources for mankind. However, competitive power of domestic mariculture industry was declined due to increase of labor and feed expenditures, and quantity import of low-priced livefishes from the developing underdeveloped nations in North and South East Asia. Mass production and quality enhancement can be pointed out to overcome such an industrial environment in this decade. To meet these requirement, movable mariculture base remodeling feasible vessel of chemical tanker or crude oil carrier has been proposed for more advanced mariculture management system by using deep seawater from about 200m which is sustainablely clean, nutrient-rich and cold seawater. Deep seawater can be applied for control of seawater temperature for mariculture base and cultivation phytoplankton and seaweed as feed. Besides mariculture, strategic marketing can be implemented by raw water and ice of deep seawater. Feasibility of applying deep seawater was considered after evaluating general movable mariculture base and management system.

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.28-34
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• 2023

Nitrogen fertilizers applied to agricultural lands for crop cultivation can be volatilized as ammonia. The released ammonia can catalyze the formation of ultrafine dust (particulate matter, PM2.5), classified as a short-lived climate change pollutant, in the atmosphere. Currently, one of the prominent methods for fertilizer application in agricultural lands is soil surface application, which comprises spraying the fertilizers onto the soil surface, followed by mixing the fertilizers with the soil. Owing to the low nitrogen absorption rate of crops, when nitrogen fertilizers are applied in this manner, they can be lost from land surfaces through volatilization. Therefore, investigating a new fertilization method to reduce ammonia emissions and increase the fertilizer utilization efficiency of crops is necessary. In this study, to develop a method for reducing ammonia emissions from nitrogen fertilizers applied to soil surfaces, deep fertilization was conducted using a newly developed deep fertilization device, and ammonia emissions from barley, garlic, and onion fields were examined. Conventional fertilization (surface application) and deep fertilization (soil depth of 25 cm) were conducted for analysis. The fertilization rate was 100% of the standard fertilization rate used for barley, and deep fertilization of N, P, and K fertilizers was implemented. Ammonia emissions were collected using a wind tunnel chamber, and quantified subsequently susing the indole-phenol blue method. Ammonia emissions released from the basal fertilizer application persisted for approximately 58 d, beginning from approximately 3 d after fertilization in conventional treatments; however, ammonia was not released from deep fertilization. Moreover, barley, garlic, and onion yields were higher in the deep fertilization treatment than in the conventional fertilization treatment. In conclusion, a new fertilization method was identified as an alternative to the current approach of spraying fertilizers on the soil surface. This new method, which involves injecting nitrogen fertilizers at a soil depth of 25 cm, has the potential to reduce ammonia emissions and increase the yields of barley, garlic, and onion.