• 농촌지도와개발
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• 제23권3호
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• pp.233-242
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• 2016

This study aimed to analyze educational satisfaction and the relationship between learning transfer and learning persistence shown after actual education targeting students who participated in the agricultural education and training. Conclusions based on the study results can be suggested as follows. First, of the factors related to learning persistence, satisfaction of educational contents turned out to be a statistically significant factor with a positive effect in the agricultural education and training. Students participating in the agricultural education and training have a conspicuous object to learn for improving ability which is necessary for and applicable to agriculture. Second, of the three factors related to learning transfer in the agricultural education and training, satisfaction of educational contents, educational facilities and satisfaction of environment turned out to have a positive effect. Third, results show that satisfaction of instructors does not affect both learning persistence and learning transfer. Lastly, in case of education and training for field practice, this study is suggesting the necessity of research by accessing in a concrete and detailed manner such as learning contents, instructors, educational facilities and satisfaction of environment from the comprehensive concept of educational satisfaction in the directivity of study related to satisfaction.

• 농촌지도와개발
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• 제22권4호
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• pp.435-444
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• 2015

This study analyzes the structural relationship between attitudes and learning more properties in agricultural college education programs. The results were as follows. first, The model was accepted according to the some goodness of fit statistics such as ${\chi}^2$(84.28, p>.05), RMR(.036), RMSEA(.041), GFI(.927), NFI(.945), CFI(.985), IFI(985). seconds, Learning attitude(.31) and content validity(.47) in the structural relationship between variables is a direct impact on satisfaction. thirds, Perceived Usefulness(.34) and Content validity(.36) has direct effect of factor on learning attitude. Finally Perceived Usefulness was found to direct effect all Content validity(.64) and easy of use(.27). Finally, considering of duties required in the agriculture. increase the satisfaction of learners should have provide field learning based Learning materials, practices, instructional media. As a result, it will enhance the performance of field learning agricultural education programs.

• 농업과학연구
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• 제47권3호
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Educational Technology International
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• 제8권2호
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• pp.53-71
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• 2007

This study identified the effectiveness of e-learning by comparing learning outcome in conventional face-to-face lecture with the selected e-learning methods. Two e-learning contents (animation based and video based) were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which is http://www.enaged.co.kr. Fifty-four Korean agricultural high school students were randomly assigned into three groups (face-to-face lecture, animation based e-learning, and video based e-learning group). The students of the e-learning group logged on the LMS in school computer lab and completed each e-learning. All students were required to take a pretest and posttest before and after learning under the direction of the subject teacher. A one-way analysis of covariance was administered to verify whether there was any difference between face-to-face lecture and e-learning in terms of students' learning outcomes after controlling the covariate variable, pretest score. According to the results, no differences between animation based and video based e-learning as well as between face-to-face learning and e-learning were identified. Findings suggest that the use of well designed e-learning could be worthy even in agricultural education, which stresses hands-on experience and lab activities if e-learning was used appropriately in combination with conventional learning. Further research is also suggested, focusing on a preference of e-learning content type and its relationship with learning outcome.

• 한국농공학회논문집
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• 제65권6호
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• 농촌지도와개발
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• 제17권1호
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• pp.75-101
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• 2010

This study tried to analyze the learning effectiveness of multi-media based class by comparing with traditional classroom method. The "Basic Agricultural Technology" course that is one of the required courses of agricultural high school was selected and its contents were digitalized on MS Powerpoint for multi-media based class. The thirty students were sampled for each experimental and control groups. The homogeneity and learning achievement of sample groups were tested for experiment. Same teacher took the classes of two groups and delivered same contents of course. Only difference between two groups was the delivery method, one is traditional classroom teaching method and the other was the multi-media based class. The learning achievements and satisfaction of sample were post-tested in order to analyze the learning effectiveness by comparing two teaching methods. The results showed that there was a significant difference between experimental and control group in learning achievement after ANCOVA controlled pre-test as covariance(F=5.08, p<.05). It means that the learning achievement of multi-media based class was higher than that of traditional classroom group. The results also showed that a significant difference in students’ satisfaction between two groups (t=5.57, p<.001). This study concluded that using multi-media in class could produce more learning achievements and satisfaction of students than traditional classroom method.

• 농업과학연구
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• 제47권2호
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• 농업과학연구
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• 제49권3호
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• pp.619-627
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• 2022

In Korea, apples and pears are among the most important agricultural products to farmers who seek to earn money as income. Generally, farmers make decisions at various stages to maximize their income but they do not always know exactly which option will be the best one. Many previous studies were conducted to solve this problem by predicting farmers' income structure, but researchers are still exploring better approaches. Currently, machine learning technology is gaining attention as one of the new approaches for farmers' income prediction. The machine learning technique is a methodology using an algorithm that can learn independently through data. As the level of computer science develops, the performance of machine learning techniques is also improving. The purpose of this study is to predict the income structure of apples and pears using the automatic machine learning solution H2O.AI and to present some implications for apple and pear farmers. The automatic machine learning solution H2O.AI can save time and effort compared to the conventional machine learning techniques such as scikit-learn, because it works automatically to find the best solution. As a result of this research, the following findings are obtained. First, apple farmers should increase their gross income to maximize their income, instead of reducing the cost of growing apples. In particular, apple farmers mainly have to increase production in order to obtain more gross income. As a second-best option, apple farmers should decrease labor and other costs. Second, pear farmers also should increase their gross income to maximize their income but they have to increase the price of pears rather than increasing the production of pears. As a second-best option, pear farmers can decrease labor and other costs.

• 농촌지도와개발
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• 제8권1호
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• pp.1-14
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• 2001

e-Learning has brought dramatic changes in education system for many companies in Korea. Many researchers and practitioners believe that e-Learning will be the main educational system for every companies in the world. e-Learning is an alternative education system, which includes computer based learning, web based learning, virtual classroom, and distance learning. e-learning has been expected to impact every educational sectors including Extension services. This study intends to identify and suggest some implications for successful e-Learning implementation of Extension education by investigating the successful factors of enterprises' e-Learning system, where outstanding results have be shown.

• 디지털융복합연구
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• 제16권12호
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• pp.293-299
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• 2018

기상은 농작물 재배에 많은 영향을 미친다. 농작물 재배지의 기상정보는 효율적인 농작물 재배 및 관리에 필수적이다. 농업기상 정보의 높은 수요에도 불구하고 이에 대한 연구는 부족하다. 본 연구는 중장기 계절예측정보인 GloSea5와 심층 신경망을 통해 양파의 주산지인 전라남도의 농업기상 정보 생산 방법을 다룬다. 연구방법으로는 매일 생산되는 GloSea5 기상정보를 훈련시키기 위해 슬라이딩 창 방법을 활용한 심층신경망 모형이 사용되었다. 모형의 정확도평가는 농업기상관측소의 일 평균기온과 GloSea5 예측값 그리고 딥러닝 예측값 차이의 RMSE와 MAE로 계산하였다. 심층신경망 모형은 학습기간이 늘어날수록 정확도가 향상되므로 학습기간과 예측기간에 따른 예측성능을 비교하였다. 분석결과 학습기간과 예측기간은 비례하지만 계절변화에 따른 추세성이 반영되는 한계점이 있었다. 이를 보안하기 위해 예측값과 관측값의 차이를 다음날 예측값에 적용시킨 후보정 심층신경망 모형을 제시하였다.