• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.366-375
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• 2022

The monitoring of electricity consumption using Internet of Things (IoT) technology is attracting attention as a technology to reduce operation costs of smart farms. In this study, we propose a method to apply a real-time electrical consumption monitoring system (the e-Gauge system) and utilization of the collected data real-time while a melon-producing smart farm is in operation. For this purpose, the electrical consumption data for the individual smart-farm facilities such as boilers, nutrient distribution systems, automatic controllers, circulation fans, boiler controllers, and other IoT-related utilities were collected during three months of melon cultivation period. By using the monitoring results, the electrical energy consumption pattern was analyzed as an example, and necessary considerations needed to optimally utilize the measurement data were suggested. This paper will be useful in lowering the technological implementation barriers for new researchers to build a electrical consumption monitoring system and reducing trial and errors in the usage of the generated data.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.63-68
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• 2018

Research on smart farms centering on greenhouse cultivation is actively under way due to the decrease in agriculture population and aging, but in the case of vegetables such as vegetables, outdoor cultivation is 70%. Therefore, there is a need to improve productivity and prevent soil contamination by automating, cultivating, and intelligentizing the outdoor cultivation of agriculture crops. In this paper, we show the case of establishing a outdoor production system using the Internet of things and define the environmental variables in the outdoor production system. By measuring soil temperature, water content, electrical conductivity and acidity through sensors, LoRa communication module transmits the information to the outdoor production system. The outdoor production system controls the amount of fertilizer and the volume of water based on this sensor data. We have developed a system that manages a wide range of crops using LoRa technology, which is a suitable communication method for cultivating crops, and manages production volume and sales performance.

• Smart Media Journal
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• v.7 no.1
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• pp.45-51
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• 2018

Avian influenza (AI) is notorious for its rapid infection rate, and has a serious impact on consumers and producers alike, especially in poultry farms. The AI outbreak, which occurred nationwide at the end of 2016, devastated the livestock farming industries. As a result, the prices of eggs and egg products had skyrocketed, and the event was reported by the media with heavy emphasis. The purpose of this study was to investigate the correlation between the egg price fluctuation and the keyword changes in online news articles reflecting social issues. To this end, we analyzed 682 cases of AI-related online news articles for fourteen weeks from November 2016 in South Korea. The results of this study are expected to contribute to understanding the relationship between the actual price of eggs and the keywords from news articles related to social issues.

• Smart Media Journal
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• v.9 no.1
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• pp.16-22
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• 2020

In this study, a neural network method performing both Detection and Classification of diseases and insects in paprika is proposed with Multi-Tasking U-net. Paprika on farms does not have a wide variety of diseases in this study, only two classes such as powdery mildew and mite, which occur relatively frequently are made as the targets. Aiming to this, a U-net is used as a backbone network, and the last layers of the encoder and the decoder of the U-net are utilized for classification and segmentation, respectively. As the result, the encoder of the U-net is shared for both of detection and classification. The training data are composed of 680 normal leaves, 450 mite-damaged leaves, and 370 powdery mildews. The test data are 130 normal leaves, 100 mite-damaged leaves, and 90 powdery mildews. Its test results shows 89% of recognition accuracy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.247-254
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• 2021

In this paper, we propose antecedent factors that influence the Information Literacy Competency of female farmers using smart devices. The study was analyzed using the materials from the "Primary Survey of Information Literacy Competency Measurement Index for Female Farmers" by the Rural Development Administration. The target of the survey was 200 women working on the farms. The descriptive, correlation, and multiple regression analyses, t-test, and ANOVA using SPSS 21.0 program were performed. Study results found that among the psychological factors, Self-efficacy and Innovativeness have positive effects on Information Literacy Competency. Among the Sociodemographic factors, the education level, presence of smartpads, and tiem spent on smartphones have positive effects on Information Literacy Competency. Findings of the results contributed to the understanding of the Information Literacy Competency for female farmers, who have lack of infromarion on smrt devices, and suggested the direction of enhancing the level of Information Literacy Competency for female farmers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.93-100
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• 2023

In this paper, the purpose of this study is to melt the process of returning to farming through games and settle down in a stable manner to ensure that there are no more prospective young farmers who wish to return to farming but cannot proceed with their dreams due to various barriers of reality. The game was designed to develop in the order of fields, greenhouses, automation systems, and smart farms, and to grow the crops they want at the early level, and added a community system to highlight that rural areas are community life, not individualistic life. Support benefits or information provided by local governments or governments were inserted into the community system so that prospective farmers could naturally access the information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1720-1728
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• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.238-245
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• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.