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Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis

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  • Eunsuk Chun

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Sungchan Jun

    (Department of Transportation and Logistics, Gyeonggi Research Institute, Jangan-gu, Suwon-si, 1150, Korea)

  • Chulung Lee

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

Abstract

In this study, we suggest methodologies for identifying promising and vacant technologies on smart farms by analyzing patent information. Additionally, a technology roadmap for smart farms is suggested using network analysis. The database of patents related to smart farms was extracted from the United States Patent and Trademark Office (USPTO) by keyword search, and valid patents data was selected and clustered using the Latent Dirichlet Allocation (LDA) algorithm. We also conducted the technical importance analysis and trend analysis to identify promising technology topics. By developing a patent map based on a self-organizing map (SOM), we were able to identify vacant technologies among smart farm technology groups. In order to develop vacant technologies, we presented a stepwise technology roadmap by analyzing the relationship between technology elements using network analysis. The proposed procedure and analysis method provides useful insights in establishing research and development (R&D) strategies for the development of smart farm technology roadmaps.

Suggested Citation

  • Eunsuk Chun & Sungchan Jun & Chulung Lee, 2021. "Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10709-:d:643949
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    as
    1. Cho, Youngsang & Hwang, Junseok & Lee, Daeho, 2012. "Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 97-106.
    2. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    3. Federica Caffaro & Eugenio Cavallo, 2019. "The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy," Agriculture, MDPI, vol. 9(5), pages 1-13, May.
    4. Harvey, Andrew C & Fernandes, C, 1989. "Time Series Models for Count or Qualitative Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 407-417, October.
    5. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    6. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
    7. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    8. Shiu-Wan Hung & An-Pang Wang, 2010. "Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 121-134, January.
    9. Jumi Hwang & Kyung Hee Kim & Jong Gyu Hwang & Sungchan Jun & Jiwon Yu & Chulung Lee, 2020. "Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    10. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    11. Jiwon Yu & Jong-Gyu Hwang & Jumi Hwang & Sung Chan Jun & Sumin Kang & Chulung Lee & Hyundong Kim, 2020. "Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    12. Harvey, Andrew C & Fernandes, C, 1989. "Time Series Models for Count or Qualitative Observations: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 422-422, October.
    13. Lee, Hakyeon & Geum, Youngjung, 2017. "Development of the scenario-based technology roadmap considering layer heterogeneity: An approach using CIA and AHP," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 12-24.
    14. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
    15. Park, Yongtae & Yoon, Byungun & Lee, Sungjoo, 2005. "The idiosyncrasy and dynamism of technological innovation across industries: patent citation analysis," Technology in Society, Elsevier, vol. 27(4), pages 471-485.
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