• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.12
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• pp.1231-1236
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• 2016

Paris Agreement signed in January 2015 is a new rule that will replace the existing Kyoto Protocol. The new agreement needs new demands and challenges to minimize carbon emissions. Especially, even though agricultural sector occupies only 1.8% in the national energy consumption, the portion of the energy being occupied in agricultural production costs very high. Although renewable energy and energy-saving facilities is being developed and disseminated for replacing fossil fuel energy and saving energy, the installation-rate is not enough high. Thus, this paper developed Korean-style smart farm system, and carried out the experiment to show the performance of energy savings through analyzing proper environment in domestic situation.

• Smart Media Journal
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• v.13 no.6
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• pp.16-23
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• 2024

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.

• Journal of IKEEE
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• v.24 no.2
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• pp.632-637
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• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.

• Journal of Environmental Science International
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• v.28 no.11
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• pp.1019-1025
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• 2019

The aim of this study was to analyze the light traits in a solar light-collector device and its effects on lettuce growth at an early growth stage. The three hyper parameters used were the reflector diameter (2 cm and 4 cm), coating inside the reflector (chrome-coated, non-coated) and distance from the light fiber (15 cm and 20 cm). The results showed that light efficiency, which is the ratio of light intensity inside the fiber to the solar intensity, improved by 41.1 % when using a 2 cm diameter chrome-coated reflector at a distance of 15 cm from the light fiber; whereas it only improved by 20.6% when a non-coated reflector was used. As the reflector size was increased to 4 cm, the light efficiency for the coated and non-coated reflectors increased by 28.5 % and 26.4 %, respectively, hence, no significant difference was observed. When the light fiber was placed at a distance of 20 cm, the increase in light efficiency with coating treatment was 8 % higher than without coating treatment. We also compared the efficiency of light-fiber treatment with that of LED treatment in our lettuce nursery, and observed that the plants exhibited better growth with light-fiber treatment. We observed an average increase of 1.7 cm in leaf height, $7cm^2/plant$ increase in leaf area, and 32 mm increase in root length upon light-fiber treatment as opposed to those observed with LED treatment. These findings indicate that the collector light-fiber is economically feasible and it improves lettuce growth compared with the LED treatment.

• Smart Media Journal
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• v.8 no.3
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• pp.88-94
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• 2019

Livestock disease is a very important issue in the livestock industry because if livestock disease is not responded quickly enough, its damage can be devastating. To solve the issues involving the occurrence of livestock disease, it is necessary to diagnose in advance the status of livestock disease and develop systematic and scientific livestock feeding technologies. However, there is a lack of domestic studies on such technologies in Korea. This paper, therefore, proposes Livestock Disease Forecasting and Livestock Farm Integrated Control System using Cloud Computing to quickly manage livestock disease. The proposed system collects a variety of livestock data from wireless sensor networks and application. Moreover, it saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system. This provides livestock disease forecasting and livestock farm integrated controlling service through MapReduce Model-based parallel data processing. Lastly, it also provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.618-620
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• 2019

Smart Farm, which combines agriculture and ICT convergence technology, is at a lower stage than other industries in Korea, but it is also one of the most active research and development fields. Smart Farm aims to improve the efficiency of each step by collecting, processing and analyzing various information of agriculture sector through convergence between agriculture and ICT technology. In this study, we studied the image processing method that can distinguish strawberry which can be harvested at harvest time by color for smart farm composition of strawberry which is a horticultural crop. Strawberry harvesting requires a lot of labor in the process of growing strawberries. In this study, we aim to collect information necessary for labor saving in strawberry harvester. As a precedent study, we plan to implement a form in which the color temperature changes according to the light direction and brightness value through OpenCV color detection using Python. In the future, it is planned to study strawberry color value suitable for harvest by applying compensation value to color temperature change.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.43-53
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• 2022

Rice paddy has been actively converted into upland crop fields as more profitable upland crop cultivation are encouraged along with the decrease in rice consumption. However, the current water supply system remains mainly for paddy water supply, so research on pipeline water supply for upland cultivation is needed. The objective of this study was to optimize storage tank installation locations for pipeline water supply in reservoir irrigation districts. Five of reservoir irrigation districts were selected as the study sites and gridded of 10×10 m in size. Then genetic algorithm was adopted to evaluate the effects of spatial storage tank allocation on total pipeline cost. The lengths of the main and branch pipelines were considered as the objective cost function for the optimization of storage tank installation. Overall the shorter the branch pipeline and the longer the main pipeline, as the number of storage tanks increase. The minimal pipeline cost, i.e., optimal condition was reached when approximately 10% of the storage tank numbers to total upland plots were installed. The methodology presented in this study can be applied to determine the number and spatial arrangement of storage tanks for upland pipeline irrigation system design.