품질경영학회지 (Journal of Korean Society for Quality Management)

  • 제49권4호
  • /
  • Pages.569-579
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  • 2021
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  • 1229-1889(pISSN)
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  • 2287-9005(eISSN)
한국품질경영학회 (Korean Society for Quality Management)
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Attention 기반 Encoder-Decoder 모델을 활용한작물의 생산량 예측

Forecasting Crop Yield Using Encoder-Decoder Model with Attention

  • 강수람 (전남대학교 수학통계학과) ;
  • 조경철 (전라남도농업기술원) ;
  • 나명환 (전남대학교 수학/통계학과)
  • Kang, Sooram (Dept. Mathematics & Statistics, Chonnam National University) ;
  • Cho, Kyungchul (Jeonnam Agricultural Research & Extension Services) ;
  • Na, MyungHwan (Dept. Statistics, Chonnam National University)
  • 투고 : 2021.11.15
  • 심사 : 2021.12.07
  • 발행 : 2021.12.31
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초록

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

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과제정보

본 논문은 농촌진흥청 공동연구사업의 지원을 받아 연구되었음(과제번호 : PJ01455903).

참고문헌

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