• 센서학회지
• /
• 제33권4호
• /
• pp.230-236
• /
• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• 디지털산업정보학회논문지
• /
• 제7권1호
• /
• pp.1-9
• /
• 2011

In this paper, Wireless sensor network technology applied to various greenhouse agro-industry items such as horticulture and local specialty etc., we was constructed automatic control system for optimum growth environment by measuring growth status and environmental change. existing monitoring systems of greenhouse gather information about growth environment depends on the temperature. but in this system, Can be efficient collection and control of information to construct wireless sensor network by growth measurement sensor and environment monitoring sensor inside of the greenhouse. The system is consists of sensor manager for information processing, an environment database that stores information collected from sensors, the GUI of show the greenhouse status, it gather soil and environment information to soil and environment(including weather) sensors, growth measurement sensor. In addition to support that soil information service shows the temperature, moisture, EC, ph of soil to user through the interaction of obtained data and Complex Growth Environment information service for quality and productivity can prevention and response by growth disease or disaster of greenhouse agro-industry items how temperature, humidity, illumination acquiring informationin greenhouse(strawberry, ginseng). To verify the executability of the system, constructing the complex growth environment measurement system using wireless sensor network in greenhouse and we confirmed that it is can provide our optimized growth environment information.

• 농업과학연구
• /
• 제46권1호
• /
• pp.57-65
• /
• 2019

The purpose of this study was to develop an environment field monitoring system to measure crop growth. The environment field monitoring system consisted of sensors, a data acquisition system, and GPS. The sensors used in the environment field monitoring system consisted of an ambient sensor, a soil sensor, and an intensity sensor. The temperature and humidity of the atmosphere were measured with the ambient sensor. The temperature, humidity, and EC of the soil were measured with the soil sensor. The data acquisition system was developed using the Arduino controller. The field monitoring data were collected before a rainy day, on a rainy day, and after the rainy day. The measured data using the environment field monitoring system were compared with the Daejeon regional meteorological office data. The correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office was analyzed for performance evaluation. The correlation of the temperature and humidity of the atmosphere was analyzed because the Daejeon regional meteorological office only provided data for the temperature and humidity of the atmosphere. The correlation coefficients were 0.86 and 0.90, respectively. The result showed a good correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office. Therefore, the developed system could be applied to monitoring the field environment of agricultural crops.

• 인터넷정보학회논문지
• /
• 제9권1호
• /
• pp.79-90
• /
• 2008

실시간적인 데이터의 수집과 더불어 수집한 데이터의 실시간적인 전송을 기반으로 사용자가 센서데이터를 보다 폭넓게 활용할 수 있는 환경을 제공하기 위해 시스템에서 자동적으로 정보를 배포해주는 정보배포 시스템의 필요성이 증대되고 있다. 이러한 요구에 맞추어 본 논문에서는 그리드 환경을 기반으로 센서네트워크에서 유입되는 방대한 양의 데이터를 처리 및 공유하기 위한 정보배포 시스템과 보다 효율적으로 데이터와 사용자의 요구를 매칭하는 방법을 제공하는 CGIM알고리즘을 제안하였다.

• 한국콘텐츠학회논문지
• /
• 제17권12호
• /
• pp.138-147
• /
• 2017

본 논문에서는 농작물의 생육 관리를 위하여 농작물에 설치된 센서 정보를 수집하고 모니터링 할 수 있는 스마트 멀티 센서와 환경 모니터링 시스템 소개하고자 한다. 기존의 농작물 모니터링 시스템에서는 각 센서의 정보를 취득하기 위해 센서를 종류별로 농작물에 설치하였다. 이 과정에서 각 센서들의 설치 비용이 발생하며 센서 설치 위치를 수동으로 설정해야 하는 번거로움이 있었다. 따라서 본 논문에서는 센서의 설치 비용을 최소화하기 위하여 센서들을 단일화한 스마트 멀티 센서를 설계 및 구현하였고 RFID 통신을 이용하여 설치된 스마트 멀티 센서의 위치 정보 및 센서 정보를 모니터링 할 수 있도록 설계하였다.

• 센서학회지
• /
• 제31권3호
• /
• pp.180-186
• /
• 2022

Photosynthetically active radiation flux density (PPFD) and daily light integral (DLI) values related to plant photosynthesis were obtained using the sunlight time and insolation data points in the agricultural weather sensor data for Jinan-gun, Jeollabuk-do, Korea from 2016 to 2020. The objective was to optimize the photo-environmental conditions for cultivating ginseng. The range of average monthly sunshine duration was 395.5-664.1 min, with the longest duration observed in June. The range of average annual accumulated daily insolation was 11.98-17.65 MJ·m^-2. The range of average daily external DLI calculated from the insolation and solar time data was 22.3-36.1 mol·m^-2·d^-1, and the annual cumulative DLI was 8,156-13,175 mol·m^-2·d^-1. Both the insolation and DLI values were the highest in 2016 and lowest in 2020. Based on the PPFD required for ginseng growth (111-185 µmol·m^-2·s^-1), the monthly average daily DLI and monthly cumulative DLI were 3.51-5.87 and 82-228 mol·m^-2·d^-1, respectively. The range of five-year average value for the external monthly cumulative DLI was 298-1,459 mol·m^-2·d^-1, and the monthly cumulative DLI values when a black double shading film and blue-white shading film were applied were 101-496 and 36-175 mol·m^-2·d^-1, respectively. A comparative analysis of DLI values indicated that shading was required to ginseng growth throughout the year under natural light. When the black double shading film was used, shading was required from March to October. When the blue-white shading film was applied from April to August, (i.e., the period with active ginseng growth) the appropriate DLI for ginseng growth could be continuously maintained. Regional weather differences due to climate change are gradually increasing, and even in one region, monthly and cumulative DLI values are different every year. Therefore, in order to implement a precise agricultural environment for ginseng cultivation, precise analysis and continuous research using agricultural weather sensor big data is required.

• 한국정보통신학회논문지
• /
• 제17권11호
• /
• pp.2686-2692
• /
• 2013

본 논문에서는 무선센서네트워크 기술을 활용하여 각종 생장환경 정보들을 수집하고 모니터링 할 수 있는 무선센서네트워크 기반의 온실환경 모니터링 시스템을 설계하고 구현하였다. 또한, 원격지에서 온실 시설내 내부 환경 및 시설제어시스템을 통합 제어 관리하기 위한 시스템을 제안한다. 본 논문에서 제안한 시스템은 넓은 온실 내부의 환경 데이터 수집 및 온실 시설 제어 서비스를 제공함은 물론 무선센서네트워크 기반 온실환경 모니터링 시스템 및 온실시설 제어를 통하여 실시간 원격 온실 통합 서비스 제공이 가능하다. 통합관리시스템을 위한 GUI 구현은 HMI기반의 시스템 모니터링부로 설계하였으며, 센서정보들은 실시간으로 통합관리시스템의 모니터링 화면을 통해 센서 결과값을 출력할 수 있음을 결과로 얻을 수 있었다.

• 한국산학기술학회논문지
• /
• 제14권10호
• /
• pp.4799-4804
• /
• 2013

저탄소 녹색 환경 조성 및 성장을 위한 그린 IT 기술 개발은 미래형 신기술 분야이다. 따라서, 본 논문에서는 응용 RFID 모듈에 대한 보안 데이터를 생성하여 건물 환경에 대한 통합 감시 및 관리를 할 수 있는 스마트 그린빌딩 조성용 다기능 센서 통합 모듈 시스템을 개발하였다. 논문에서 구현한 다기능 센서 통합 모듈 시스템은 열 감지센서, 온도 감지센서, 스모그 감지센서, CO2 감지센서, O2 감지센서, 장력 감지센서 및 파손 감지센서를 통합 모듈로 개발하고 이를 실시간으로 모니터링 해줌으로써 건물 내부 환경에 대한 스마트 그린빌딩 환경을 구현할 수 있는 시스템을 설계, 개발하였다.

• Journal of Multimedia Information System
• /
• 제4권2호
• /
• pp.93-98
• /
• 2017

A new cultivation diary system based on environment sensor data and Web 2.0 with Flex is suggested, to improve the previous system using the subjective data of cultivators. The proposed system is designed by applying an object-oriented model called mini-architecture, in order to enhance the reliability of software as well as promote stability to overall system design. The environment sensor data such as temperature and humidity are used to develop the new reliable diary. Also, an active interface based on Web 2.0 and Android as the user GUI are implemented to maximize the convenience while recording the cultivation diary. The result of the performance evaluation shows that the data from sensors has 99.1% of correlation with that of analogue.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권8호
• /
• pp.2259-2277
• /
• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.