• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.86-92
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• 2017

In this study, a model to classify frost occurrence and frost free day was developed using the digital weather forecast data provided by Korea Meteorological Administration (KMA). The minimum temperature, average wind speed, relative humidity, and dew point temperature were identified as the meteorological variables useful for classification frost occurrence and frost-free days. It was found that frost-occurrence date tended to have relatively low values of the minimum temperature, dew point temperature, and average wind speed. On the other hand, relatively humidity on frost-free days was higher than on frost-occurrence dates. Models based on machine learning methods including Artificial Neural Network (ANN), Random Forest(RF), Support Vector Machine(SVM) with those meteorological factors had >70% of accuracy. This results suggested that these models would be useful to predict the occurrence of frost using a digital weather forecast data.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of Agricultural Extension & Community Development
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• v.9 no.2
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• pp.199-213
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• 2002

The purpose of this study uses agricultural knowledge system theory to explore how the extension system in South Korea was developed and have worked well. By agricultural knowledge system we emphasized the dynamic networks of actors, processes of negotiation, and the diverse ways in which knowledge is constructed and performed. It was possible that individuals may participate in and utilize multiple knowledge systems. The knowledge systems reflected the idea that the boundaries between knowledge groups were not closed and that there could be considerable overlap between knowledge systems. The constructions of agricultural knowledge systems thus included social interactions, communication, and the diverse processes individuals employ to create, use, and evaluate multiple types and sources of information. As such, there were six priorities to development agricultural extension system; the linkage between agricultural colleges, Rural Development Administration(RDA), branch of RDA, establishing the research institution of research and extension linkage. exchange research agent with extension agent, developing information technology system, bottom-up approach, the linkage between national project and regional within extension projects, enforcement of informal learning.

• Journal of the Korean School Mathematics Society
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• v.3 no.2
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• pp.123-131
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• 2000

In order to educate future leaders of the new age, we should help students to increase their basic knowledge, thinking and problem solving ability. It is necessary that we should use multi-media, computer as well as old teaching-learning material to improve students' basic knowledge and to motivate their interest in mathematics in the small-sized Middle School situated on the agricultural and fishery village. In solving this problem, it is ultimately necessary that we should utilize CAI program on the learning achievement in mathematics for the students to understand basic concept, principle, law and to promote teaching-learning process considered on individual different abilities. Therefore, this study is on the effect of students' interest and learning achievement in mathematics when we develop CAI program focusing on the lesson statistics in the 3rd Grade Middle School Mathematics Textbook and explain the concept and principle of statistics through using exact and various techniques of computers

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.481-493
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• 2020

Deep learning models, which imitate the function of human brain, have drawn attention from many engineering fields (mechanical, agricultural, and computer engineering etc). The major advantages of deep learning in engineering fields can be summarized by objects detection, classification, and time-series prediction. As well, it has been applied into environmental science and engineering fields. Here, we compiled our previous attempts to apply deep learning models in water-environment field and presented the future opportunities.

• Journal of Agricultural Extension & Community Development
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• v.8 no.2
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• pp.179-190
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• 2001

The purpose of this study was to analysis the education effects of web-based on-line cyber program mesaured by Kirkpatrick’s evaluation process. The average score on satisfaction of the program was 4.28(.59), which was designed to evaluate the level 1, reaction. To test level 2, learning, the average score that students achieved was calculated and it was 86.87(std.=7.05) in the term examinations. The level 3, job months. It was reported that most employees who took the course are utilizing the knowledge that they acquired from the course(mean=3.80, std.=.77). To identify the level 4, business results, the mean score of the number of accidents and near misses that happened in their factories for 3 months before and after the course were compared. There was statistically significant difference between the number of accidents that happened 3 months before the course and 3 months after the course, at the significance level of .01, which was tested by Paired t-test.

• Journal of Agricultural Extension & Community Development
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• v.22 no.3
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• pp.321-332
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• 2015

For the purpose of enhancing the use of social service in classrooms, this research focuses on the relationships between presence and learning flow, key words in the analysis of college classes using Facebook. The results of this study are as follow. First, social presence(${\ss}=.33$, p=.000), emotional presence(${\ss}=.29$, p= .000), cognitive presence(${\ss}=.20$, p= .010) were found to be significant according to cognitive flow experience the result of analysis of multiple regression. all regression coefficients were positive. Second, emotional presence(${\ss}=.42$, p=.000) and social presence(${\ss}=.27$, p=.000), cognitive, presence(${\ss}=.17$, p=.015) were found to be significant according to emotional flow experience the result of analysis of multiple regression. all regression coefficients were positive. Third, social presence(${\ss}=.37$, p=.000) of the three variables were found to be significant according to behavioral flow experience the result of analysis of multiple regression.

• Journal of Agricultural Extension & Community Development
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• v.20 no.3
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• pp.603-642
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• 2013

Community Business is defined as profit-making enterprise for which a community residents can take to solve their own problems. It is comprised of some sequential activities: identifying problems, collective learning, organization. In rural South Korea, the central and local governments are promoting Community Businesses. However, the related policy programs are missing the very important perspective that self-help approach be essential in Community Business. Therefore, the policy programs should be changed so that they could effectively help community's autonomous practice.