DOI QR코드

DOI QR Code

A Study on the Development of Artificial Intelligence Crop Environment Control Framework

  • Guangzhi Zhao (Dept. of Computer Science and Engineering, Jeonbuk National University)
  • Received : 2023.03.18
  • Accepted : 2023.03.26
  • Published : 2023.05.31

Abstract

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

Keywords

Acknowledgement

This work was supported by project for Joint Demand Technology R&D of Regional SMEs funded by Korea Ministry of SMEs and Startups in 2023.(Project No. RS-2023-00207672)

References

  1. O'Shaughnessy, Susan A., et al. "Towards smart farming solutions in the US and South Korea: A comparison of the current status." Geography and Sustainability 2021. https://doi.org/10.1016/j.geosus.2021.12.002
  2. De Vries, Albert, Nikolay Bliznyuk, and Pablo Pinedo. "Invited Review: Examples and opportunities for artificial intelligence (AI) in dairy farms." Applied Animal Science 39.1, 2023: 14-22. https://doi.org/10.15232/aas.2022-02345
  3. Minsky, Marvin. "Future of AI technology." 1992.
  4. Zimmermann, H-J. "Fuzzy set theory." Wiley interdisciplinary reviews: computational statistics 2.3, 2010: 317-332. https://doi.org/10.1002/wics.82
  5. Fan, Jianqing, Fang Han, and Han Liu. "Challenges of big data analysis." National science review 1.2, 2014: 293-314. https://doi.org/10.1093/nsr/nwt032
  6. Boursianis, Achilles D., et al. "Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review." Internet of Things 18, 2022: 100187. https://doi.org/10.1016/j.iot.2020.100187
  7. Malrey Lee, "Final Report for Small and Medium Business Administration: IOT-based artificial intelligence development of a framework for environmental control of wood ear mushrooms," Oct. 2019.
  8. S. H. Jang, K. W. Nam, and Y. G. Jung, "Smart Building Block Toys using Internet of Things Technology," International Journal of Advanced Culture Technology, vol. 4, no. 2, pp. 34-37, Jun. 2016. https://doi.org/10.17703/IJACT.2016.4.2.34
  9. Madakam, Somayya, et al. "Internet of Things (IoT): A literature review." Journal of Computer and Communications 3.05, 2015: 164. https://doi.org/10.4236/jcc.2015.35021 
  10. Jo, Jae-Yeong. "Water quality of agricultural groundwater in Western Coast area and Eastern Mountain Area of Jeollabuk-do." Journal of Applied Biological Chemistry 54.3, 2011: 218-224. https://doi.org/10.3839/jabc.2011.036
  11. Arita, I. K. U. O. Pholiota nameko. New York, NY: Academic Press, 1978.
  12. De Boer, Pieter-Tjerk, et al. "A tutorial on the cross-entropy method." Annals of operations research 134, 2005: 19-67. https://doi.org/10.1007/s10479-005-5724-z