• Journal of Mushroom
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• v.18 no.3
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• pp.274-279
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• 2020

This study was conducted to investigate the characteristics of the medium used on the composting step, comparing the excavator agitator with the self-propelled turner. The temperature of the outdoor composting medium tended to increase rapidly after flipping in the turner. The late composting medium temperature was maintained at the excavator treatment area (farm practice), and the late composting effect progressed. During the field composting stage, various microorganisms such as Bacillus spp., Actinomycetes, fluorescent Pseudomonas spp., and filamentous fungi were distributed in the medium, and the density of aerobic bacteria involved in the decomposition of the medium was increased. Under high-temperature composting conditions, blue fungi, and mesophilic actinomycetes were inhibited or killed. Thermophilic actinomycetes, which play an important role in decomposing organic matter, showed higher densities than those observed in farm practices in the self-propelled turner process. The length of rice straw was slightly shorter when the self-propelled turner was used, and the water content did not show any significant difference between treatments. The a and b values tended to increase as the inverter was turned over. The CN ratio of the composting broth was lowered from 23.1 to 16.2 for the 5th turnover in the context of farming practices, and from 23.3 to 16.9 in the context of the self-propelled turner. The yield of each treatment was increased by 20% in 1 period, 28% in 2 periods, and 26% in 3 periods; the overall yield was 23%.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.107-119
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• 2019

As the development of ICT and integration technology, many changes and innovations in agriculture field are implemented. The agricultural sector has shifted from a traditional industry to a new industrial form called the 6th industry combined with various advanced technologies such as ICT and IT. Various approaches have been attempted to analyze and predict crops based on spatial information. In particular, a variety of research has been carried out recently for crop cultivation and smart farms using drones. The goal of this study was to establish an agricultural drought monitoring system using drones to produce scientific and objective indicators of drought. A soil moisture sensor was installed in the drought area and checked the actual soil moisture. The soil moisture data was used by the reference value to compare and analyze the temperature and NDVI established by drones. The soil temperature by the drone thermal image sensor and the NDVI by the drone hyperspectral was analyzed the correlation between crop condition and soil moisture in study area. To verify this, the actual soil moisture was calculated using the soil moisture measurement sensor installed in the target area and compared with the drone performance. This study using drone drought monitoring system may enhance to promote the crop data and to save time and economy.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• Land and Housing Review
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• v.15 no.2
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• pp.107-124
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• 2024

Based on Muan-gun, Jeollanam-do, this study explores how to mitigate the disappearance of rual areas. The study surveyed 95 young farmers in Muan-gun to assess their farming practices and the challenges they face. We further employ factor analysis and cluster analysis classify young farmers in Muan-gun, facilitating the identification of tailored policies or initiatives aimed at fostering and supporting young farmers. The results are summarized as follows. First, Muan County does not have any ordinances or original projects specifically designed to support young farmers. Second, the succession rate of farmland among young farmers in Muan County is 41.1%, which is comparable to the national rate of 43.7%. This indicates that approximately 40% of young farmers in Korea have inherited farmland, a critical foundation for agricultural activities. Third, despite accumulating farming experience, young farmers have not seen any improvement in local living conditions, and rather their difficulties have intensified. Fourth, this study conducted a factor analysis using 21 variables, resulting in the selection of seven common factors for cluster analysis. Consequently, young farmers in Muan County were categorized into three groups. The multinomial logit analysis revealed that the typology of young farmers is influenced by indicators such as cultivated area, farming experience, demand for smart farms, farm income, and farming type (rice cultivation or other). Therefore, to attract young farmers and prevent the decline of rural areas, policy efforts should focus on minimizing entry barriers to farming infrastructure, such as access to farmland, and improving local settlement conditions.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.117-125
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• 2019

This study compared the temperature and humidity measured by an aspirated radiation shield (ARS), the accuracy of which has been recently verified, and those measured by a system developed by the parent company (Company A) to investigate and improve the performance of the developed system. The results are as follows. Overall, the two-plate system had a lower radiation shielding effect than the one-plate system but showed better performance results when excluding the effect of strawberry vegetation on the systems. The overall maximum temperature ranges measured by company A's system and the ARS were $20.5{\sim}53.3^{\circ}C$ and $17.8{\sim}44.1^{\circ}C$, respectively. Thus, the maximum temperature measured by company A's system was $2.7{\sim}9.2^{\circ}C$ higher, and the maximum daily temperature difference was approximately $12.2^{\circ}C$. The overall average temperature measured by company A's system and the ARS was $12.4{\sim}38.6^{\circ}C$ and $11.8{\sim}32.7^{\circ}C$, respectively. Thus, the overall average temperature measured by company A's system was $0.6{\sim}5.9^{\circ}C$ higher, and the maximum daily temperature difference was approximately $6.7^{\circ}C$. The overall minimum temperature ranges measured by company A's system and the ARS were $4.2{\sim}28.6^{\circ}C$ and $2.9{\sim}26.4^{\circ}C$, respectively. Thus, the minimum temperature measured by company A's system was $1.3{\sim}2.2^{\circ}C$ higher, and the minimum daily temperature difference was approximately $2.9^{\circ}C$. In addition, the overall relative humidity ranges measured by company A's system and the ARS were 52.9~93.3% and 55.3~96.5%, respectively. Thus, company A's system showed a 2.4~3.2% lower relative humidity range than the ARS. However, there was a day when the relative humidity measured by company A's system was 18.0% lower than that measured by the ARS at maximum. In conclusion, there were differences in the relative humidity measured by the two company's devices, as in the temperature, although the differences were insignificant.

• Journal of Applied Biological Chemistry
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• v.65 no.3
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• pp.129-142
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• 2022

Ginseng sprouts, which can be eaten from leaves to roots, has the advantage of not having to use pesticides without being affected by the season by using smart farms. The optimal cultivation timing of sprout ginseng was checked and the nutritional content and antioxidant activity were compared and analyzed. The values of total fatty acids and total minerals were no significant changes during the growth periods. The contents of total amino acids were slightly decreased to 45 days and after increased to 65 days. When the growth period was 65 days, arginine had the highest content of 3309.11 mg/100 g. The total phenolic contents were high at 3.73 GAE mg/g on the 45 days, and the total flavonoid contents were also the highest at 9.04 RE mg/g on the 45 days. The contents of total ginsenoside was not noticeable for the growth periods (29.83 on 25 days→32.77 on 45 days→26.02 mg/g on 65 days). The ginsenoside Rg2 (0.62 mg/g), Re (8.69 mg/g), Rb1 (4.75 mg/g) and Rd (3.47 mg/g) had highest contents on 45 days during growth. The values of phenolic acids and flavonols were gradually increased to 45 days (338.6 and 1277.14 ㎍/g) and then decreased to 65 days. The major compounds of phenolic acids and flavonols were confirmed to benzoic acid (99.03-142.33 ㎍/g) and epigallocatechin (416.03-554.64 ㎍/g), respectively. The values of 2,2-diphenyl-1-picrylhydrazyl (44.27%), 2,4,6-azino-bis (3-ethylbenzothiazoline-6-sulphnoic acid) diammonium salt (75.16%), and hydroxyl (63.29%) radical scavenging activities and ferric reducing/antioxidant power (1.573) showed the highest activity on the 45 days as well as results of total phenolic and total flavonoid contents.