Attention 기반 Encoder-Decoder 모델을 활용한 작물의 생산량 예측 (Forecasting Crop Yield Using Encoder-Decoder Model with Attention)

서지정보

· 발행기관 : 한국품질경영학회
· 수록지 정보 : 품질경영학회지 / 49권 / 4호 / 569 ~ 579페이지
· 저자명 : 강수람, 조경철, 나명환

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.

영어초록

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.

참고자료

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