• Journal of Agricultural Extension & Community Development
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• v.16 no.2
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• pp.463-486
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• 2009

The purpose of this study was to find out the differences of preferences in e-Learning contents delivery types according to information searching retrieval ability in agricultural high school students. Contents delivery types are limited three kinds which are HTML type, video type, and text type and need to know about differences. The following summarizes the results of this study. On the preference of e-Learning contents delivery type on information searching retrieval ability had differences. High level group of information searching retrieval ability showed that they mostly preferred text contents delivery type. However, low level group of information searching retrieval ability showed that they preferred video contents delivery type. The results support our belief that there could be the differences in preferences in e-Learning delivery types with students' information searching retrieval abilities. We suggest that delivery types of e-Learning should be based on the students not on designers and developers.

• Journal of Agricultural Extension & Community Development
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• v.11 no.1
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• pp.1-19
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• 2004

The objective of this study was to find a linkage of ideological background between agricultural extension education and lifelong education. This study was conducted by analyzing the studies related to agricultural extension and lifelong education. Review of literature and documents was main methods of this study. The study reviewed and analyzed the concepts, characteristics and ideology of lifelong education, and presented some general characteristics of lifelong education in the context of educational ideology. As a result of the study, the following five characteristics of lifelong education in the context of educational ideology were presented; 1) lifelong education is the supreme concept of education and includes all kinds of education, 2) lifelong education is the future direction of educational ideology and philosophy rather than a kind of educational practice, 3) lifelong education means the security for a right of learning through the entire life-span of an individual, 4) lifelong education has the innovative function of the existing situation of education; viewpoint, contents, and methodology of learning, 5) Lifelong education runs ultimately towards a 'learning society'. Agricultural extension and lifelong education shared the similar ideological background in general, and have the similar basic philosophy. The ideology and philosophy of lifelong education should be reflected into the ideology of agricultural extension to broaden the perspectives of agricultural extension in the future.

• Animal Bioscience
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• v.35 no.9
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• pp.1454-1460
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• 2022

Objective: Early environmental enrichment in life can improve cognition in animals. The effect of prenatal auditory stimulation on learning ability and fear level in chick embryos remained unexplored. Therefore, this study investigated the effect of prenatal auditory stimulation on the learning ability and fear level of chicks. Methods: A total of 450 fertilized eggs were randomly divided into 5 groups, including control group (C), low-sound intensity music group (LM), low-sound intensity noise group (LN), high-sound intensity noise group (HN) and high-sound intensity music group (HM). From the 10th day of embryonic development until hatching, group LM and group LN received 65 to 75 dB of music and noise stimulation. Group HN and group HM received 85 to 95 dB of noise and music stimulation, and group C received no additional sound. At the end of incubation, the one-trial passive avoidance learning (PAL) task and tonic immobility (TI) tests were carried out, and the serum corticosterone (CORT) and serotonin (5-HT) concentrations were determined. Results: The results showed that compared with the group C, 65 to 75 dB of music and noise stimulation did not affect the PAL avoidance rate (p>0.05), duration of TI (p>0.05) and the concentration of CORT (p>0.05) and 5-HT (p>0.05) in chicks. However, 85 to 95 dB of music and noise stimulation could reduce duration of TI (p<0.05) and the concentration of CORT (p<0.05), but no significant effect was observed on the concentration of 5-HT (p>0.05) and PAL avoidance rate (p>0.05). Conclusion: Therefore, the prenatal auditory stimulation of 85 to 95 dB can effectively reduce the fear level of chicks while it does not affect the learning ability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Journal of Agricultural Extension & Community Development
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• v.23 no.4
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• pp.449-457
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• 2016

The purpose of this study are to examine the educational use of the Facebook and to examine the effects of the learners perceived usefulness, attitude and self-efficacy on learning flow and learning presence at university classes using social network service. The subjects of this study are 155 university students attending a class at a 4-year university in Seoul, and certain questions verified in the existing studies were modified, complemented, and used as a tool for measurement. The details of the purpose are as follows. First, it turned out that there were significant differences in learning flow and learning presence in accordance with the levels of the usefulness that learners perceived. It can be said that the higher the perceived level of usefulness, the higher the learning flow and learning presence. Second, it turned out that there were significant differences in learning flow and learning presence in accordance with the levels of learners' perceived attitudes. We can interpret such a result as suggesting that the higher a learner's perceived attitude, the higher the learning flow and learning presence.

• Journal of Agricultural Extension & Community Development
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• v.24 no.1
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• pp.21-31
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• 2017

The purpose of this study was to analyze the structural relationships between adult educators' instructional leadership, learners' motivation and performance among those participants in education for urban-rural migration. The survey was conducted among 22 agricultural educational institutions in South Korea, and a total number of 1,109 learners responded to a questionnaire. In order to verify the hypothesized research model, the collected data were analyzed with structural equation modeling. The major findings of this study were as follows. First, adult educators' instructional leadership had a direct effect and an indirect effect on learners' performance. Second, adult educators' instructional leadership had a direct effect on learners' motivation. Third, learners' motivation had a direct effect on learners' performance.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.