• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2007년도 춘계학술발표회
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• pp.49-50
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• 2007

• Korean Journal of Agricultural Science
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• 제49권4호
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.

• Journal of the Korean School Mathematics Society
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• 제1권1호
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• pp.121-130
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• 1998

The purpose of this study is to enhance the achievement in mathematics by developing compensatory learning materials and applying those to learning. The conclusions of this study are as follows. 1. Solving the learning tasks by cooperative learning guided instruction to learning by students from teaching by teacher, and it made learning effective and students cooperative in personal-relation 2. Learning materials for compensating learning deficit made students motivated and interested in mathematics, and active in learning. 3. By applying the learning materials for compensating prerequisite learning deficit, students could grasp learning contents and learning tasks, and their achievement could be enhanced. On the basis of problems which appeared in the progress of this study, the suggestions are as follows. 1, The necessity of mathematics in agricultural high schools should be recognized by students and various learning materials should be developed. 2. In cooperative learning, the roles of team-chiefs have a great influence on learning mood and problem-solving processes, they, therefore, must be directed beforehand so that they play the roles of leaders.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• Journal of Agricultural Extension & Community Development
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• 제29권1호
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• pp.19-31
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• 2022

This study aims to examine the moderating effect of regional agricultural leader's capacity between the sense of community of female farmers and the resident participation. A survey was conducted on 312 female farmers from October 20 to November 19, 2020. The main results of the analysis are as follows. First, it showed that the higher the sense of community, the higher the awareness of resident participation. Second, it was found that the sense of community had a positive effect on resident participation as self-directed learning capability was higher, and that self-directed learning capability had a moderating effect on the relationship between the sense of community and the resident participation. Third, regional agricultural leadership capacity was found to have a moderating effect in the relationship between the sense of community and the resident participation.

• Journal of Agricultural Extension & Community Development
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• 제22권2호
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• pp.233-243
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• 2015

This study is for understanding differences of satisfaction followed by learning and teaching presence perceived by learners and participation motivation on learning in a distance education. General characteristics of learner are classified as an entrance type, sex, age, new/transfer of distance university learners. Perceived presence is classified with learning presence and teaching presence. Participation motivation on learning is classified with the directivity of activity, goal, and study based on the reason of participating in classes in a distance university. And this research tried out to find the effects of learner's characteristics, perceived presence, and participation motivation on learning satisfaction. The results are as follows. First, there was no meaningful difference of general characteristic on satisfaction. It means sex, age, and entrance type did not have an effect on satisfaction. Second, with the level of presence perceived by learner, satisfaction has meaningful differences. It means that students who had higher learning presence perceived of distance university showed higher satisfaction, and so as in teaching presence perceived on satisfaction. Third, factors effecting satisfaction based on participation motivation on learning differ with types of motivation. There was no meaningful difference of the level of activity directivity study participant on satisfaction, but was a meaningful difference of goal directivity and of study directivity in participation motivation on learning satisfaction. It was the learning presence that had a significant effect on learning satisfaction of adult learning.

• Magazine of the Korean Society of Agricultural Engineers
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• 제66권2호
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• pp.30-38
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• 2024

• Journal of Agricultural Extension & Community Development
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• 제21권1호
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• pp.35-82
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• 2014

It was main objectives to find the learners characteristics and educational effects of cyber agricultural technology courses in RDA. For the research, it was followed by literature reviews and internet based survey methods. In internet based survey, two staged stratified sampling method was adopted from cyber training members database in RDA along with some key word as open course or certificate course, and enrollment years. Instrument was composed through literature reviews about cyber education effects and educational effect factors. And learner characteristics items were added in survey documents. It was sent to sampled persons by e-mail and 316 data was returned via google survey systems. Through the data cleaning, 303 data were analysed by chi-square, t-test and F-test. It's significance level was .05. The results of the research were as followed; First, the respondent was composed of mainly man(77.9%), and monthly income group was mainly 2,000,000 or 3,000,000 won(24%), bachelor degree(48%), fifty or forty age group was shared to 75%, and their job was changed after learning(12.2%). So major respondents' job was not changed. Their major was not mainly agriculture. Learners' learning style were composed of two or more types as concrete-sequential, mixing, abstract-random, so e-learning course should be developed for the students' type. Second, it was attended at 3.2 days a week, 53.53 minutes a class, totally 172.63 minutes a week. They were very eager or generally eager to study, and attended two or more subjects. The cyber education motives was for farming knowledge, personal competency development, job performance enlarging. They selected subjects along with their interest. A subject person couldn't choose more subjects for little time, others, non interesting subject, but more subject persons were for job performance benefits and previous subjects effectiveness. Most learner was finished their subject, but a fourth was not finished for busy (26.7%). And their entrying behavior was not enough to learn e-course and computer or internet using ability was middle level as software using. And they thought RDA cyber course was comfort in non time or space limit, knowledge acquisition, and personal competency development. Cyber learning group was composed of open course only (12.5%), certificate only(25.7%), both(36.3%). Third, satisfaction and academic achievement of e-learning learners were good, and educational service offering for doing job in learning application category was good, but effect of cyber education was not good, especially, agricultural income increasing was not good because major learner group was not farmer, so they couldn't apply their knowledge to farming. And content structure and design, content comprehension, content amount were good. The more learning subject group responded to good in effects, and both open course and certificate course group satisfied more than open course only group. Based on the results, recommendation was offered as cyber course specialization before main course in RDA training system, support staff and faculty enlargement, building blended learning system with local RDA office, introducing cyber tutor system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of The Korean Society of Agricultural Engineers
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• 제64권1호
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• pp.39-50
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• 2022

In this study, we suggested the optimal training period for predicting the streamflow using the LSTM (Long Short-Term Memory) model based on the deep learning and CMIP5 (The fifth phase of the Couple Model Intercomparison Project) future climate scenarios. To validate the model performance of LSTM, the Jinan-gun (Seongsan-ri) site was selected in this study. We comfirmed that the LSTM-based streamflow was highly comparable to the measurements during the calibration (2000 to 2002/2014 to 2015) and validation (2003 to 2005/2016 to 2017) periods. Additionally, we compared the LSTM-based streamflow to the SWAT-based output during the calibration (2000~2015) and validation (2016~2019) periods. The results supported that the LSTM model also performed well in simulating streamflow during the long-term period, although small uncertainties exist. Then the SWAT-based daily streamflow was forecasted using the CMIP5 climate scenario forcing data in 2011~2100. We tested and determined the optimal training period for the LSTM model by comparing the LSTM-/SWAT-based streamflow with various scenarios. Note that the SWAT-based streamflow values were assumed as the observation because of no measurements in future (2011~2100). Our results showed that the LSTM-based streamflow was similar to the SWAT-based streamflow when the training data over the 30 years were used. These findings indicated that training periods more than 30 years were required to obtain LSTM-based reliable streamflow forecasts using climate change scenarios.