• Korean Journal of Medicinal Crop Science
• /
• v.24 no.4
• /
• pp.277-283
• /
• 2016

Background: The photosynthetic efficiency cool-season, semi-shade ginseng is normal at low morning temperatures, but drops at high afternoon temperatures. Therefore, optimal plant performance would be ensured if it were possible to control daily light transmission rates (LTR). Methods and Results: Plants were grown in a controlled light environment that replicated 11 AM conditions and comparatively analyzed against plant grown under normal conditions. Growth in the controlled light environment resulted in a 2.81 fold increase in photosynthetic efficiency with no change in chlorophyll content, although LTR were high due to low morning temperatures. Increased aerial plant growth was observed in the ginseng plants adapted to the controlled light environment, which in turn influenced root weight. An 81% increase in fresh root weight (33.3 g per plant on average) was observed in 4-year-old ginseng plants grown in controlled light environment compared to the plants grown following conventional practices (18.4 g per plant on average). With regard to the inorganic composition of leaves of 4-year-old ginseng plants grown in controlled light environment, an increased in Fe content was observed, while Mn and Zn content decreased, and total ginsenoside content of roots increased 2.37 fold. Conclusions: Growth of ginseng under a favorable light environment, such as the condition which exist naturally at 11 AM and are suitable for the plant's photosynthetic activity creates the possibility of large scale production, excellent-quality ginseng.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.357-359
• /
• 2022

The market for eco-friendly food materials by online distribution is rapidly growing due to major environmental pollution such as air, soil, and water quality, and radical changes in living patterns caused by COVID-19. In addition, because of the aging population and the decrease in agricultural-related population due to social structural changes, aquaponics is emerging as a system that can solve problems such as independence of old economic activities, environmental protection, and securing healthy and safe food. This paper aims to design an intelligent plant growth measurement system among intelligent aquaponics production management modules for optimal growth environment derivation and quantitative production prediction by converging various ICT technologies into existing aquaponics systems. In particular, the focus is on designing systems suitable for production sites that do not have high-performance processing resources, and we propose a module configuration plan for production environments and training data and prediction systems.

• Fisheries and Aquatic Sciences
• /
• v.20 no.11
• /
• pp.30.1-30.10
• /
• 2017

Small-scale fish farmers in developing countries are faced with challenges owing to their limited information on aquaculture management. Nile tilapia farmers in Teso North Sub-County recorded lower yields than expected in 2009 despite having been provided with required inputs. Water quality was suspected to be the key factor responsible for the low yields. This study sought to assess the effects of earthen pond water physico-chemical parameters on the growth of Nile tilapia in six earthen fish ponds under semi-intensive culture system in Teso North Sub-County. The study was longitudinal in nature with pond water and fish being the units of analysis. Systematic sampling was used to select five ponds while a control pond was purposively selected based on its previously high harvest. Four ponds were fed by surface flow and two by underground water. Each pond was fertilized and stocked with 900 fry of averagely 1.4 g and 4.4 cm. Physico-chemical parameters were measured in-situ using a multi-parameter probe. Sixty fish samples were randomly obtained from each pond fortnightly for four months using a 10 mm mesh size and measured, weighed and returned into the pond. Mean range of physico-chemical parameters were: dissolved oxygen (DO) 4.86-10.53 mg/l, temperature $24-26^{\circ}C$, pH 6.1-8.3, conductivity $35-87{\mu}S/cm$ and ammonia 0.01-0.3 mg/l. Temperature (p = 0.012) and conductivity (p = 0.0001) levels varied significantly between ponds. Overall Specific Growth Rate ranged between 1.8% (0.1692 g/day) and 3.8% (1.9 g/day). Ammonia, DO and pH in the ponds were within the optimal levels for growth of tilapia, while temperature and conductivity were below optimal levels. As temperature and DO increased, growth rate of tilapia increased. However, increase in conductivity, pH and ammonia decreased fish growth rate. Temperature and DO ranging between 27 and $30^{\circ}C$ and 5-23 mg/l, respectively, and SGR of 3.8%/day and above are recommended for higher productivity.

• Journal of Bio-Environment Control
• /
• v.14 no.4
• /
• pp.269-274
• /
• 2005

The purpose of this experiment was to investigate optimal ionic concentration of nutrient solution for water culture of young welsh onion (Allium fistulosum). For the purpose of clarification of optimal nutrient concentration to maximize growth of young welsh onion, different nutrient concentrations of Yamazaki's solution for welsh onion seedling $(NO_3^--N\;9.0,\;NH_4^+-N\;3.0,\;PO_4^{3-}-P\;6.0,\;K^+7.0,\;Ca^{2+}\;2.0,\;Mg^{2+}\;2.0,\;and\;SO_4^{2-}-S\;4.4me{\cdot}L^{-1})$ which selected by prior experiment were treated as 0.6, 1.2, 1.8, and $2.4dS{\cdot}m^{-1}$. Increments of fresh weight, dry weight and top length were the highest in 1.2 and, in the next, were placed by the order of 1.8, 2.4, and $0.6dS{\cdot}m^{-1}$ The regression coefficients for the maximal growth of fresh weight of cv. 'Geurnjanguedaepa' and 'Tokyokuro' were $y=-42.091x^2+171.79x+11.047 (R^2=0.8946,\; R=0.9458^*)\;and\;y=-50.069x2+157.58x+15.414(R^2=0.9343,\;R=0.9692^{**})$, respectively, and optimal EC levels according to regression coefficients were 1.68 and $1.57dS{\cdot}m^{-1}$. As the conclusions, optimal nutrient levels far young welsh onion were $1.2dS{\cdot}m^{-1}$ EC in the early growth stage and $1.6\~l .7dS{\cdot}m^{-1}$ in the later growth stage.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1427-1435
• /
• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• International journal of advanced smart convergence
• /
• v.9 no.1
• /
• pp.132-140
• /
• 2020

It is very important to use appropriate nutrition water for crop growth in hydroponic farming facilities. However, in many cases, the supply of nutrition water is not designed with a precise plan, but is performed in a conventional manner. We proposes a forecasting technique for nutrition water requirements based on a data analysis for optimal strawberry production. To do this, the proposed forecasting technique uses linear regression for correlating strawberry production, soil condition, and environmental parameters with nutrition water demand for the actual two-stage strawberry production soil. Also, it includes predicting the optimal amount of nutrition water requires according to the heterogeneous cultivation environment and variety by comparing the amount of nutrition water needed for the growth and production of different kinds of strawberries. We suggested study uses two types of section beds that are compared to find out the best section bed production of strawberry growth. The dataset includes 233 samples collected from a real strawberry greenhouse, and the four predicted variables consist of the total amounts of nutrition water, average temperature, humidity, and CO₂ in the greenhouse.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.9
• /
• pp.355-362
• /
• 2022

Smart Farm, which combines information and communication technologies with agriculture is moving from simple monitoring of the growth environment toward discovering the optimal environment for crop growth and in the form of self-regulating agriculture. To this end, it is important to collect related data, but it is more important for farmers with cultivation know-how to analyze the collected data from various perspectives and derive useful information for regulating the crop growth environment. In this study, we developed a web service that allows farmers who want to obtain necessary information with data related to crop growth to easily analyze data. Web-based data analysis serivice developed uses R language for data analysis and Express web application framework for Node.js. As a result of applying the developed data analysis service together with the growth environment monitoring system in operation, we could perform data analysis what we want just by uploading a CSV file or by entering raw data directly. We confirmed that a service provider could provid various data analysis services easily and could add a new data analysis service by newly adding R script.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.4
• /
• pp.155-159
• /
• 2018

In this paper, we designed and built a system that is site manager can be monitored remotely to the growth of the fishery environment. To this end, modular distribution system and environmental monitoring of live fishery products. Simulation results, were building a purification system and the oxygen supply to the fishery living inside the container was to build a system that can be monitored in real time. As a result, it was able to raise the necessary environment for the storage and distribution of fisheries products. We constructed a monitoring system with database and communication environment and created the optimal environment suitable for growth with sensor. As a result, it was possible to monitor the environment for the storage and distribution of fishery products and to build a suitable fishery products management system.

• The Korean Journal of Malacology
• /
• v.16 no.1_2
• /
• pp.25-29
• /
• 2000

In order to investigate the optimal environment factor for growth of the cockle shell, Anadara granosa bisenensis, tolerance experiment to the water temperature and salinity have been conducted. In the tolerance experiment to the temperature A. granosa bisenensis survived a very wide range from 3$^{\circ}C$ to 33$^{\circ}C$, but their survival rate was lower at higher water temperature up to 35$^{\circ}C$. In the case of salinity their tolerance range was from 0$\textperthousand$ to 100$\textperthousand$ and showed higher survival rate at lower salinity. Growth in shell length and total weight of the spat at the end of the rearing experiment was 13.17${\pm}$0.98 mm and 0.69${\pm}$011 g.

• ALGAE
• /
• v.31 no.1
• /
• pp.61-72
• /
• 2016

Arctic microalgae thrive and support primary production in extremely cold environment. Three Arctic green microalgal strains collected from freshwater near Dasan Station in Ny-Alesund, Svalbard, Arctic, were analyzed to evaluate the optimal growth conditions and contents of fatty acids. The optimal growth temperature for KNF0022, KNF0024, and KNF0032 was between 4 and 8℃. Among the three microalgal strains, KNF0032 showed the maximal cell number of 1.6 × 107 cells mL^-1 at 4℃. The contents of fatty acids in microalgae biomass of KNF0022, KNF0024, and KNF0032 cultured for 75 days were 37.34, 73.25, and 144.35 mg g^-1 dry cell weight, respectively. The common fatty acid methyl esters (FAMEs) analyzed from Arctic green microalgae consisted of palmitic acid methyl ester (C16:0), 5,8,11-heptadecatrienoic acid methyl ester (C17:3), oleic acid methyl ester (C18:1), linoleic acid methyl ester (C18:2), and α-linolenic acid methyl ester (C18:3). KNF0022 had high levels of heptadecanoic acid methyl ester (26.58%) and heptadecatrienoic acid methyl ester (22.17% of the total FAMEs). In KNF0024 and KNF0032, more than 72.09% of the total FAMEs consisted of mono- and polyunsaturated fatty acids. Oleic acid methyl ester from KNF0032 was detected at a high level of 20.13% of the FAMEs. Arctic freshwater microalgae are able to increase the levels of polyunsaturated fatty acids under a wide range of growth temperatures and can also be used to produce valuable industrial materials.