Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지
DOI QR코드

DOI QR Code

Forecasting Crop Yield Using Encoder-Decoder Model with Attention

Attention 기반 Encoder-Decoder 모델을 활용한작물의 생산량 예측

  • Kang, Sooram (Dept. Mathematics & Statistics, Chonnam National University) ;
  • Cho, Kyungchul (Jeonnam Agricultural Research & Extension Services) ;
  • Na, MyungHwan (Dept. Statistics, Chonnam National University)
  • 강수람 (전남대학교 수학통계학과) ;
  • 조경철 (전라남도농업기술원) ;
  • 나명환 (전남대학교 수학/통계학과)
  • Received : 2021.11.15
  • Accepted : 2021.12.07
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

Acknowledgement

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

References

  1. Alhnaity, B., Pearson, S., Leontidis, G., and Kollias, S. 2020. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments. Acta Hortic 1296:425-432. https://doi.org/10.17660/actahortic.2020.1296.55
  2. Chen, T., Yin, H., Chen, H., Wu., L., Wang, H., Zhou, X., and Li, X. 2018. TADA : Trend Alignment with Dual-Attention Multi-Task Recurrent Neural Networks for Sales Prediction. 2018 IEEE International Conference on Data Mining 49-58.
  3. Chen, Y., Lin and W., Wang, J. 2019. A dual-Attention-Based Stock Price Trend Prediction Model With Dual Features. IEEE Access 7:148047-148058. https://doi.org/10.1109/access.2019.2946223
  4. Choudhury, A. and Jones, J. 2014. Crop Yield Prediction Using Time Series Models, Journal of Economic and Economic Education Research 15(3):53-68.
  5. Chung, D. and Han, D. 2018. Evaluation of Forecasting Performance of Rice Yield Models under Climate Change. Korea Environmental Policy And Administration Society 26(4):197-222. https://doi.org/10.15301/jepa.2018.26.4.197
  6. Dharmaraja, S., Jain, V., Anjoy, P., and Chandra, H. 2020. Empirical Analysis for Crop Yield Forecasting in India, Agric Res. 9(1):132-138. https://doi.org/10.1007/s40003-019-00413-x
  7. Farook, A., J., Kannan, K., S. 2014. Climate Change Impact on Rice Yield in India-Vector Autoregression Approach, Sri Lankan Journal of Applied Statistics 16(3):161-178. https://doi.org/10.4038/sljastats.v16i3.7830
  8. Feng, L., Zhao, C., Sun, Y. 2020. Dual attention-based encoder-decoder: A customized sequence-to-sequence learning for soft sensor development. IEEE Transactions on Neural Networks and Learning Systems 1-12.
  9. Han, M. and Yu, S. 2019. Prediction of Baltic Dry Index by Application of Long Short-Term Memory. Journal of Korean Society for Quality Management 47(3):497-508. https://doi.org/10.7469/JKSQM.2019.47.3.497
  10. Hossain, M., M., Abdulla, F. 2015. On the Production Behaviors and Forecasting the Tomatoes Production in Bangladesh Journal of Agricultural Economics and Development 4(5):66-74.
  11. Jiang, Z., Liu, C., Ganapathysubramanian, B. Hayes, and D., Sarkar, S. 2020. Predicting county-scale maize yields with publicly available data, Scientific Reports 10:14957. https://doi.org/10.1038/s41598-020-71898-8
  12. Kim, N. and Lee, Y. 2016. Experimental Predictions of Crop Yields Using Time-Series Modeling of Climate Reanalysis Data: A Case of Iowa, USA, 1960-2009. The Korean Cartographic Association 16(2):115-126.
  13. Lee, S., Yoon, Y, Jung, J., Sim, H., Chang, T., and Kim, Y. 2020. A Machine Learning Model for Predicting Silica Concentrations through Time Series Analysis of Mining Data. Journal of Korean Society for Quality Management 48(3):511-520. https://doi.org/10.7469/JKSQM.2020.48.3.511
  14. Li, L., Wu, Y., Zhang, Y., and Zhao, T. 2018. Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series IEEE Access 7:17644-17653. https://doi.org/10.1109/access.2019.2895897
  15. Na, M., Cho, W., and Kim, S. 2020. A Construction of Web Application Platform for Detection and Identification of Various Diseases in Tomato Plants Using a Deep Learning Algorithm. Journal of Korean Society for Quality Management 48(4):58-596.
  16. Oh, S. and Kim, M. 2017. Predicting Onion Production by Weather and Spatial Time Series Model. Journal of The Korean Data Analysis Society 19(5):2447-2456. https://doi.org/10.37727/jkdas.2017.19.5.2447
  17. Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, and G., Cottrell, G. 2017. A dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. arXiv preprint:1704.02971.
  18. Shook, J., Gangopadhyay, T., and Wu, L., Ganapathysubramanian, B., Sarkar, S., Singh, A., K. 2020. Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning arXiv preprint:2006.13847.