• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.15-26
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• 2023

This study aimed to evaluate the performance of water level classification from CCTV images in agricultural facilities such as reservoirs. Recently, the CCTV system, widely used for facility monitor or disaster detection, can automatically detect and identify people and objects from the images by developing new technologies such as a deep learning system. Accordingly, we applied the ResNet-50 deep learning system based on Convolutional Neural Network and analyzed the water level of the agricultural reservoir from CCTV images obtained from TOMS (Total Operation Management System) of the Korea Rural Community Corporation. As a result, the accuracy of water level detection was improved by excluding night and rainfall CCTV images and applying measures. For example, the error rate significantly decreased from 24.39 % to 1.43 % in the Bakseok reservoir. We believe that the utilization of CCTVs should be further improved when calculating the amount of water supply and establishing a supply plan according to the integrated water management policy.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.3
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• pp.1-14
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• 2024

Gaining an accurate 3D stream geometry has become feasible with Unmanned Aerial Vehicle (UAV), which is crucial for better understanding stream hydrodynamic processes. The objective of this study was to investigate series of filters to remove stream vegetation and propose the best method for generating Digital Terrain Models (DTMs) using UAV-based point clouds. A stream reach approximately 500 m of the Bokha stream in Icheon city was selected as the study area. Point clouds were obtained in August 1st, 2023, using Phantom 4 multispectral and Zenmuse L1 for Structure from Motion (SfM) and Light Detection And Ranging (LiDAR) respectively. Three vegetation filters, two morphological filters, and six composite filters which combined vegetation and morphological filters were applied in this study. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used to assess each filters comparing with the two cross-sections measured by leveling survey. The vegetation filters performed better in SfM, especially for short vegetation areas, while the morphological filters demonstrated superior performance on LiDAR, particularly for taller vegetation areas. Overall, the composite filters combining advantages of two types of filters performed better than single filter application. The best method was the combination of Progressive TIN (PTIN) and Color Indicies of Vegetation Extraction (CIVE) for SfM, showing the smallest MAE of 0.169 m. The proposed method in this study can be utilized for constructing DTMs of stream and thus contribute to improving the accuracy of stream hydrodynamic simulations.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.177-183
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• 2019

We intend to improve the sustainability of rural villages by investigating the element technologies and platforms necessary for building smart villages. There are so many investment smart city platforms and solutions in many cities. but there are relatively few investments in rural or small cities. This situation can not only increase the urban problem due to the increase of population to the city, but also deepen the digital gap of citizens. So far, studies on smart village have been investigated in fragments. We will examine the cases applied to smart village as a whole and study the open smart village platform that analyzes the overall data storage and management of the village after the smart village was finally established. First, we will look at the overseas trends of smart village and second, we will study the smart village platform that efficiently manages smart village through the technology necessary for smart village.

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.148-148
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• 2021

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.21-30
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• 2022

Renewable energy systems aim to achieve carbon neutrality and replace fossil fuels. Photovoltaic technologies are the most widely used renewable energy. However, they require a large operating area, thereby decreasing available farmland. Accordingly, agrivoltaic systems (AVSs)-innovative smart farm technologies that utilize solar energy for crop growth and electricity production-are attracting attention. Although several empirical studies on these systems have been conducted, comprehensive research on their design is lacking, and no standard model suitable for South Korea has been developed. Therefore, this study created an integral design of AVS reflecting domestic crop cultivation conditions and conducted a structural analysis for safety assessment. The shading ratio, planting distance, and agricultural machinery work of the system were determined. In addition, national construction standards were applied to evaluate their structural safety using a finite element analysis. Through this, the safety of this system was ensured, and structural considerations were put forward. It is expected that the AVS model will allow for a stable utilization of renewable energy and smart farm technologies in rural areas.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.37-43
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• 2019

The existing smart farm technology has been systematized for the mass production rather than the consumer. There are many problems such as economical aspect to apply to actual rural environment due to aging. The purpose of this study is to apply smart farm technology based on the applicability of population aged in rural areas. Due to the heat wave, the crops in general greenhouse cultivation facilities suffered from damage such as sunlight damage. To minimize such damage, adjust the temperature and humidity environment or install a light-shielding film. However, the workers in the rural areas are aging and the elderly who are farming alone have a lot of difficulties in doing so. In the case of people with weak physical strength, there is a danger that they may lead to safety accidents when carrying heavy loads. In this paper, we propose 'Smart Palm capable of automatic transportation function', applying small smart vehicles that follow workers to existing smart farms to improve and prevent these problems. It is a smart farm that performs the control functions of the existing smart greenhouse environment, installs the rail for each trough, and has a vehicle that follows the worker. The smart app can directly control the greenhouse and the vehicle remotely manually.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.97-104
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• 2021

Despite the practical limitations of calculating the amount of inflow and supply related to the operation of agricultural reservoirs, the role of agricultural reservoirs is gradually being emphasized. In particular, as interest in disaster safety has increased, the demand for preliminary measures to prepare for disasters has been rising, for instance, pre-discharging agricultural reservoirs for flood control. The aim of this study is to analyze the plans for the flood season reservoir operation considering pre-discharge period and water level limit. Accordingly, we optimized the simulation of daily storage using the ratio correction factor (RCFs) and analyzed the amount of inflow and supply using K-HAS. In addition we developed the drought determination coefficient (k) as a indicator of water availability and applied it for supplementing the risk level criteria in the Drought Crisis Response Manual. The results showed that it would be difficult to set the water level limit during the flood period in the situation of little water supply for flood control in agricultural reservoirs. Therefore, it is necessary to operate the reservoir management regulations after measures such as securing additional storage water are established in the future.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.81-97
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• 2024

This study examines the effects of adopting Information and Communication Technology (ICT) in livestock farm management on farm sales revenue. Using the 2020 Census of Agriculture, Forestry, and Fisheries, a nationally representative data set constructed by Statistics Korea, this study focuses on a sample of 9,020 livestock farms in South Korea. We employ Propensity Score Matching (PSM) methods to address the potential selection bias between 2,076 farms that used ICT for livestock farm management and 6,944 farms that did not. The findings consistently show that the use of ICT significantly increases farm revenue, taking into account the selection bias. The utilization of ICT in livestock farms leads to a higher increase in sales revenue, particularly for farms with greater sales.

• Electronics and Telecommunications Trends
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• v.33 no.2
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• pp.77-85
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• 2018

At present, agriculture in Korea is experiencing difficulties, such as a stagnation in farm income, an increase in imported agricultural products, a decrease in arable land, a decrease in the self-sufficiency rate of grain, a decrease in rural population, and aging. To solve these problems and vitalize the rural economy, our government is promoting its 6th industrialization policy, which links agriculture with primary industry, secondary, industry and tertiary industry, and as well as smart agriculture based on information and communication technology. Smart agriculture is an agriculture form used to improve the quality of life in rural areas through making greater efficiency and intelligence by applying ICT convergence technology to the whole entire process of agricultural production, distribution, and consumption in the areas of outdoor agriculture, facility horticulture, and livestock. Therefore, in this paper, we analyze the policy, technology, and standardization trends of domestic and foreign smart agriculture, and suggest ways to apply them to domestic smart agriculture during the in the introduction stage.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.