• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.405-407
• /
• 2017

The environmental change is affecting the farm products like tomato, and pepper, etc. This affects to lead smart farming yield. What is more, this inconstant conditions cause the farms to be infected by variety diseases. Therefore ICT technology is needed to detect and prevent the crops from being effected by diseases. This article suggests the procedure to help producer for identifying farms disease based on the detected image. This detects the kind of diseases with comparing the trained image data before and after disease emergence. First step monitors an image of farms and resize it. Its features are extracted on parameters such as color, and morphology, etc. The next steps are used for classification to classify the image as infected or non-infected. on the bassis of detection algorithm.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.229-230
• /
• 2019

본 논문에서는 해수면 온도에 따라 양식장의 깊이를 연장하여 어족자원을 보호하기 위한 스마트 양식장 깊이 제어 시스템을 제안한다. 가두리 양식장의 경우 이상 고온현상에 의하여 해수면 온도가 상승하면 어족자원의 집단폐사를 가져온다. 해수면 온도가 어족자원의 폐사를 유발할 수 있는 온도에 이르면 사용자에게 통지하고 양식장의 깊이를 5m 연장시켜 어족자원이 뜨거운 해수면을 피할 수 있도록 하며, 어족자원의 수확을 필요로 할 때에는 양식장을 해수면방향으로 당길 수 있는 시스템이다.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.3
• /
• pp.177-183
• /
• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.145-148
• /
• 2022

본 논문은 스마트 팜 교육용 시스템을 IoT 기반으로 임베디드 환경에서 설계 및 구현하였다. 시스템 환경은 라즈베리파이 기반에서 노드-레드(Node-RED) 프로그램을 적용하였고 생육재배에 필요한 환경 센서와 구동장치를 연동하여 구축하였다. 센싱 정보는 유. 무선통신으로 서버로 전달되며, 데이터베이스를 근간으로 한 웹 설계는 PHP, MySQL을 사용하고, 스마트 폰 앱은 안드로이드 스튜디오를 사용하였다. 시스템 동작 과정은 센서값 인지 및 비주얼한 표시, 연동 구동장치 제어 프로그래밍, 서버 및 스마트 폰을 이용한 원격제어와 모니터링 과정을 수행할 수 있도록 설계하였다.

• Smart Media Journal
• /
• v.13 no.2
• /
• pp.23-31
• /
• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• Smart Media Journal
• /
• v.11 no.5
• /
• pp.9-16
• /
• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.19-29
• /
• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.5
• /
• pp.27-39
• /
• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.12
• /
• pp.1231-1236
• /
• 2016

Paris Agreement signed in January 2015 is a new rule that will replace the existing Kyoto Protocol. The new agreement needs new demands and challenges to minimize carbon emissions. Especially, even though agricultural sector occupies only 1.8% in the national energy consumption, the portion of the energy being occupied in agricultural production costs very high. Although renewable energy and energy-saving facilities is being developed and disseminated for replacing fossil fuel energy and saving energy, the installation-rate is not enough high. Thus, this paper developed Korean-style smart farm system, and carried out the experiment to show the performance of energy savings through analyzing proper environment in domestic situation.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.16-23
• /
• 2024

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.