• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Smart Media Journal
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• v.11 no.7
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• pp.94-103
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• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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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.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.483-488
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• 2018

Far field input utterance is one of the major causes of performance degradation of speaker verification systems. In this study, we used teacher student learning framework to compensate for the performance degradation caused by far field utterances. Teacher student learning refers to training the student deep neural network in possible performance degradation condition using the teacher deep neural network trained without such condition. In this study, we use the teacher network trained with near distance utterances to train the student network with far distance utterances. However, through experiments, it was found that performance of near distance utterances were deteriorated. To avoid such phenomenon, we proposed techniques that use trained teacher network as initialization of student network and training the student network using both near and far field utterances. Experiments were conducted using deep neural networks that input raw waveforms of 4-channel utterances recorded in both near and far distance. Results show the equal error rate of near and far-field utterances respectively, 2.55 % / 2.8 % without teacher student learning, 9.75 % / 1.8 % for conventional teacher student learning, and 2.5 % / 2.7 % with proposed techniques.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.178-192
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• 2018

The purposes of this study were to develop and apply on learning on geological field trip utilizing the social construction of scientific model. We developed field trip places by considering not only Orion (1993)'s novelty space but also the achievement standards of 2015 national curriculum. The subjects of the study were 8 in the 'G' science gifted education center. We conducted a study using the theme of 'How was formed Mt. Gwanak?' on 5 lessons including a series of 2 field trip lessons and 3 lessons utilizing the social construction of scientific model. Students participated in pre- and post-test on the understanding of scientific knowledge about formation of mountain. Semi-structured interview was used to analyze students' learning about geological field trip in terms of affective domain. Results were as follows. First, there were 2 places of upper-stream valley and down-stream valley separately. They contained outcrops gneiss, granite, joint in the valley, xenolith, fault plane, mineral in the valley. Second, pre- and post-test and semi-structure interview were analyzed in terms of what scientific knowledge students learned about and how Mt. Gwanak was formed. Seven students explained that Mt. Gwanak was volcano during pretest. Seven students described how granite was formed to form Mt. Gwanak. They also understood geological time scale, i.e., metamorphic rock. Third, the geological field trip was effective to low achievement geoscience students as they engaged in the activities of field trip. Using positive responses on affective learning was effective on learning on geological field trip when utilizing the social construction of scientific model. This study suggests that teachers use an example 'model' on geoscience education. This study also suggests that teachers apply the social construction of scientific model to geological field trip.

• Journal of Digital Convergence
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• v.6 no.3
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• pp.13-22
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• 2008

The purpose of this study is to explore 'Flow Experience' of those studying English speaking and listening in the e-Learning environment. The exploration of flow experience in this study is based on the literature research of Csikszentmihalyi's flow models and other studies. There have been many studies on flow experience focusing on arts, leisure and sports in accordance with Csikszentmihalyi's original theory, however, his flow theory has recently been adapted to the educational field. Nonetheless, it is in the e-learning environment, rather than the face-to-face traditional teaming environment, that there is not enough flow state measurement scale. Therefore, it is important to develop as a stepping stone a flow state scale for those who study English speaking and listening by the cyber-native-speaker on e-Learning environment to improve their satisfaction and achievement.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• Journal of Society of Preventive Korean Medicine
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• v.11 no.2
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• pp.113-120
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• 2007

The Internet is increasingly changing our lives and enables the ordinary person to have access to never-ending quantities of information and knowledge. Technology and the Internet empower individuals and facilitate a more active role in the educational process. The webcast is a media file distributed over the Internet using streaming media technology and is used extensively in the commercial sector for investor relations presentations, in e-learning, and for related communications activities. And relating to computers, technology, and news are particularly popular and many new shows are added regularly. Especially e-learning is a general term used to refer to computer-enhanced learning and has the ability to level the learning playing field. So that the e-learning experience will be second nature to the growing Internet population. And this study intends to develop web-based education.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.471-479
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• 2011

The world has witnessed a phenomenal growth in information and communication technologies (ICT). The development of new broadband communication services convergence of telecommunication with computers and recent developments in the field of communication protocol have fostered challenges as well as offerings in the wide-ranging use of ICT to support the creation of dynamic and interactive teaching and learning environment. By emergence of ICT, most of education and training institutions are struggling to integrate ICT and education and training systems. It is so important to apply proper delivery systems for utilization of the computerized teaching and learning contents in the classrooms. It will be a preliminary activity for building open and flexible distance teaching and learning systems in developing countries. In this paper, we would like to suggest and recommend the proper delivery systems for utilization of the computerized teaching and learning contents in the classrooms for education sector reform in developing countries.

• Sleep Medicine and Psychophysiology
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• v.7 no.1
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• pp.10-17
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• 2000

To investigate the neurobiological bases of learning and memory is one of the ambitious goals of modern neuroscience. The progress in this field of recent years has not only brought us closer to understanding the molecular mechanism underlying long-lasting changes in synaptic strength, but it has also provided further evidence that these mechanisms are required for memory formation. Since twenty years ago, several studies for the tests of the hypothesis that NMDA-dependent hippocampal long-term potentiation(LTP) underlies learning have been reported. Also, in the recent year, data from mutant mice showed that a potential role for NMDA-dependent LTP in hippocampal CA1 and spatial learning. Although the current evidence for the role of NMDA receptor in learning and memory is not still obvious, NMDA receptor seems to act as a critical switch for activation of a cascade of events that underlie synaptic plasticity.