DOI QR코드

DOI QR Code

스마트기기를 이용한 주기별 식물 생장 인식 자동 제어 모니터링 시스템

Cycle-by-Cycle Plant Growth Automatic Control Monitoring System using Smart Device

  • 김경옥 (순천대학교 컴퓨터과학과) ;
  • 김응곤 (순천대학교 컴퓨터과학과)
  • 투고 : 2013.04.06
  • 심사 : 2013.05.20
  • 발행 : 2013.05.31

초록

최근 수행된 많은 연구에서 시설 하우스나 식물 공장과 같이 실용적인 원예 시설에 대한 환경 제어 시스템이 다양하게 제시되었다. 그러나 아직까지도 식물의 전 생장 과정에 따른 온 습도 등 제어가 제대로 되지 않아 성장 장해 및 병충해에 노출되어 농가의 적지 않은 피해가 보고되고 있다. 공기 순환팬, 산업용 제습기 등을 활용하여 대책을 마련해 보고 있지만, 기대에 미치지 못하고 있다. 본 논문에서는 주기별 생장 인식 알고리즘을 이용하여 각 식물의 성장 단계를 인식하고 식물의 성장 단계에 따른 최적의 환경을 제공한다. 주기별 식물 생장 인식 자동 제어 모니터링 시스템을 이용하면 식물의 생장에 필요한 최적 환경을 제공하므로 생산성을 높일 수 있다.

In many recent studies, a variety of environmental control system for practical gardening facilities such as facility house and plant factory have been proposed. However, the plants have been exposed to growth disorder and disease and pest injury because the temperature and humidity have not properly controlled so far. Therefore, a lot of damage of farmers have been reported. The air circulation fan and industrial dehumidifier have been currently utilized as the countermeasures, but they do not meet the expectation. In this study, the growth phase of each plant is recognized by using cycle-by-cycle plants growth recogniztion algorithm to provide optimal environment according to the growth phases of each plant.he productivity can be raised by using cycle-by-cycle plant growth recognition monitoring system because it optimally controls the environment by cycle that is required for plant growth.

키워드

참고문헌

  1. D.Lowe Distinctive image features from scaleinvariant keypoints IJCV, 60(2), pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  2. K. Mikolajczyk, C. Schmid, An affine invariant interest point detector, in: ECCV, pp. 128-142, 2002.
  3. JAIN, R. AND NAGEL. H. On the analysis of accumulative difference pictures from image sequences of real world scenes. IEEE Trans. Patt. Analy. Mach. Intell. 1, 2, pp. 206-214, 1979.
  4. WREN, C., AZARBAYEJANI, A., AND PENTLAND, A. 1997. Pfinder : Real-time tracking of the human body. IEEE Trans. Patt. Analy. Mach. Intell. pp. 780-785. 1997.
  5. PARAGIOS, N. AND DERICHE, R. Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vision 46, 3, pp. 223-247, 2002. https://doi.org/10.1023/A:1014080923068
  6. OLIVER, N., ROSARIO, B., AND PENTLAND, A. A bayesian computer vision system for modeling human interactions. IEEE Trans. Patt. Analy. Mach. Intell. 22, 8, pp. 831-843. 2000. https://doi.org/10.1109/34.868684
  7. TOYAMA, K., J. KRUMM, B. B., AND MEYERS, B. Wallflower : Principles and practices of background maintenance. In IEEE International Conference on Computer Vision (ICCV). pp. 255-261, 1999.
  8. MONNET, A., MITTAL, A., PARAGIOS, N., AND RAMESH, V. Background modeling and subtraction of dynamic scenes. In IEEE International Conference on Computer Vision (ICCV). pp. 1305-1312, 2003.
  9. Xun Wag and Jian-qiu Jin. "An Edge Detection Algorithm Based on Improved CANNY Operator", 7th International Conterence on Intelligent Systems Design and Applications, pp. 623-628, Jun, 2007.
  10. P.Viola and M.J. Jones, "Robust real-time object detection", Technical Report Series, Compaq Cambridge research Laboratory, CRL 2001/01, Feb. 2001.
  11. kyeong-og Kim, Kyoung-wook Park, "Establishment of Web-based Remote Monitoring System for Greenhouse Environment", The Journal of Korea Institute of Electronic Communication Sciences, Vol. 6, No. 1, pp. 77-83, 2011.
  12. kyeong-og Kim, Kyeong-jin Ban, "Design and Implementation of System for Sensing Data Collection in RFID/USN", The Journal of Korea Institute of Electronic Communication Sciences, Vol. 5, pp. 221-226, 2010.
  13. Jong-Gil Han, Kyoung-Wook Park, "Outdoor Augmented Reality based 3D Model Visualization System of Cultural Heritage Sites", The Journal of Korea Institute of Electronic Communication Sciences, Vol. 8, pp. 459-464, 2013. https://doi.org/10.13067/JKIECS.2013.8.3.459