• Journal of Agricultural Extension & Community Development
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• v.12 no.1
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• pp.119-127
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• 2005

이 연구의 목적은 2003년부터 농촌진흥청에서 이루어져 왔던 이러닝시스템에 대한 실태와 참여농가의 만족도를 파악하고 향후 이러닝시스템의 발전방향을 모색하는 데 있다. 농촌진흥청은 2003년부터 웹과 비디오화상시스템에 기반을 둔 이러닝시스템(e-learning system)을 이용하여 농가에 대한 지도와 교육사업을 전개하여 왔다. 2003년에는 이러닝을 통한 교육과정에 251명의 농민이 등록하였으며, 이 중 최종적으로 126명이 오이, 고추, 토마토에 대한 생산과 경영관리 교육과정을 수료하였다. 2004년에는 503명의 농민이 이러닝 교육과정에 등록하였으며, 이중 313명의 농가가 버섯, 딸기, 수박에 대한 생산과 경영관리 교육과정을 수료하였다. 이러닝 교육과정에 참여하는 농민의 76%가 이러닝시스템을 통한 교육에 만족하는 것으로 나타났다. 이러닝을 위한 시스템 형태에 따른 선호도는 비디오화상시스템만을 이용한 경우는 23%. 웹만을 이용한 경우는 13%, 이 두 시스템을 통합하여 운영한 경우에는 59%로서 통합시스템운영이 더 효과적인 것으로 나타났다. 현재 진흥정이 구축한 통합시스템은 동시에 1000명 이상의 농민을 대상으로 교육을 실시할 수 있다. 지도사업의 예산과 인원이 과거에 비해 줄어들고 있는 상황에서 이러닝시스템은 향후 농민들을 대상으로 한 지도와 교육에 효과적인 대안이 될 수 있다.

• 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 Agricultural Extension & Community Development
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• v.16 no.4
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• pp.939-965
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• 2009

The Machine Learning has been identified as a promising approach to knowledge-based system development. This study aims to examine the ability of machine learning techniques for farmer's decision making and to develop the reference model for using pig farm data. We compared five machine learning techniques: logistic regression, decision tree, artificial neural network, k-nearest neighbor, and ensemble. All models are well performed to predict the sow's productivity in all parity, showing over 87.6% predictability. The model predictability of total litter size are highest at 91.3% in third parity and decreasing as parity increases. The ensemble is well performed to predict the sow's productivity. The neural network and logistic regression is excellent classifier for all parity. The decision tree and the k-nearest neighbor was not good classifier for all parity. Performance of models varies over models used, showing up to 104% difference in lift values. Artificial Neural network and ensemble models have resulted in highest lift values implying best performance among models.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.13-23
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• 2024

Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.

• Journal of Sericultural and Entomological Science
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• v.51 no.2
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• pp.114-118
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• 2013

This study examines how an experimental-based learning program using singing insects improves the academic achievement of elementary school children. Study subjects were a total of 123 elementary students at their $2^{nd}$ and $3^{rd}$ grade who voluntarily participated in an experience-based learning program organized by Seodaemun Museum of Natural History, Seoul. Students were given the same survey questionnaires before and after taking the learning program, and this procedure was repeated in 7 independent replication trials. Result from a paired t-test indicated that the learning program had a positive effect on academic performance, with students gaining significantly higher mean scores in the survey test after taking the program than before. This result suggests that the experience-based learning program using singing insects is effective at improving student's academic achievement. Our study provides a critical impetus for developing a variety of other experience-based learning programs using insects like ours, leading us to anticipate that these programs will be practiced more systematically and actively in the classroom in future.

• Magazine of the Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.42-47
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• 2024

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.81-89
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• 2012

This paper presents a set of controlled simulated statical and engineering mechanical experiments accessible via the virtual world environment (VWE) and virtual physics lab S/W. Online courses of the university offering courses and/or programs online are growing and the number of students want education in ways which fit their personal places, e-learning is becoming more important and ubiquitous each year. In this study, first of all, question is rather 'How do we execute the learning effectiveness of e-learning courses?' than 'Why does they need e-learnig or VW-learning?'. In particular, is it possible to effectively teach mechanical engineering courses online? The answer was 'No'. So, there is little research on many of these questions. And another important question is 'Is e-learning cost effective?'. For the answer, This research provided that an instructional design model is used to 'How to think and apply the Newtonian forces' in the virtual physics lab S/W. Collected data from student are administered in the spring semester when students studied 'Introduction to Bio-resources and Systems Engineering'. Results show that a cadre of students can take highly interactively physical properties of mechanical engineering in the virtual laboratory environment. Those show that VWE is greater than that of a similar real world presentation or experimental lab, since most of students are delighted to modify and retry modeling works in the VWE.

• Journal of Drive and Control
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• v.20 no.3
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• pp.25-34
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• 2023

In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R²), the test samples showed an R² of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• International Journal of Industrial Entomology and Biomaterials
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• v.37 no.2
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• pp.69-72
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• 2018

A new silkworm variety 'Chilseongjam' was bred for special purpose as educational learning and festivals. Their breeding history and major characteristics are as follows. The Chilseongjam variety was selected and succeeded from the F1 of Galwon ${\times}$ C721 in 2009 autumn. They are showing 94% (spring & autumn) of high practical hatching ratio. The larval period of Chilseongjam (spring: 23 d, autumn: 24 d 3 h) was shorter than that of Daebakjam (spring: 24 d, autumn: 25 d 23 h). In the pupation percentage, Chilseongjam (spring: 96.6%, autumn: 86.1%) is similar to Daebakjam Daebakjam (spring: 96.3%) in spring, but autumn is lower than Daebakjam (autumn: 94.9%). Single cocoon weight (spring: 1.57 g, autumn: 1.29 g) and Cocoon yield (spring: 14.2 kg, autumn: 11.1 kg) were lower than those of Daebakjam (spring: 2.76 g, 25.4 kg, autumn: 2.19g, 20.2 kg), respectively. The new silkworm variety, Chilseongjam showed higher pupation rate than control variety. This variety can be used as a educational learning and festivals.