本研究試以雙向長短期記憶模型（Bidirectional Long Short-Term memory）加上注意力機制（Attention）嘗試解決時間序列資料，跨越多時間區間上的障礙。並嘗試創造出一模型架構預測作物產期，來解決傳統的耕作上，對於產期預測方面多以耕作者的經驗來預測之問題，因作物生長需考慮天氣環境等特徵對農作物生長方面造成的影響，所以本研究採用中央氣象局所提供之天氣資料結合政府產銷履歷資料庫，以青江菜為例，結合人工智慧及機器學習方法，並應用在傳統農業上，解決了傳統農業無法以資訊化的方式來預測的問題，且也結合Attention方法改善了傳統RNNs模型問題。

This study attempts to solve the span problem of time series data with Bidirectional Long Short-Term Memory and Attention funtion. A model structure is create to predict the crop production period. At present the traditional farming’s production period mostly based on the experience of farmer, so the study try to solve this problem. In addition the crop growth needs to consider the characteristics of the weather environment. Therefore this study uses the weather data provided by the Central Meteorological Administration and government’s crop period database. The study will use Qingjiang cuisine as an example, combined with artificial intelligence and machine learning methods. And solves the problem of traditional agriculture also improves the RNNs model problem by combining attention methods. The study applied to traditional agriculture makes that more evolved and intelligent in the future.

中文文獻：
[1] 於家斌、尚方方、王小藝、許繼平、王立、張慧妍、鄭蕾，(2018)基於遺傳算法改進的一階滯後濾波和長短期記憶網絡的藍藻水華預測方法，計算機應用，第三十八期，2119-2123。
[2] 蘇偉、魯劍巍、周廣生、李小坤、李雲春、劉曉偉，(2011)稻草還田對油菜生長土壤溫度及濕度的影響，植物營養與肥料學報，第十七期，366-373。
[3] 陸佩玲、於強、賀慶棠，(2006)植物物候對氣候變化的響應，生態學報，第二十六期，923-929。
[4] 盧存福、賁桂英，(1995)高海拔地區植物的光合特性，植物學報，第十二期，38-42。
[5] 李典謨、王莽莽，(1986)快速估計發育起點及有效積溫法的研究，昆蟲知識，第二十三期，184-187。
 
英文文獻：
[1] Anderson, P. K., Cunningham, A. A., Patel, N. G., Morales, F. J., Epstein, P. R., & Daszak, P. (2004). Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends in ecology & evolution, 19(10), 535-544.
[2] Altché, F., & de La Fortelle, A. (2017). An LSTM network for highway trajectory prediction. In 2017 IEEE 20th International Conference on Intelligent Transportation.
[3] Chen, K., Zhou, Y., & Dai, F. (2015). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE International Conference on Big Data.
[4] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[5] Cornia, M., Baraldi, L., Serra, G., & Cucchiara, R. (2018). Predicting human eye fixations via an lstm-based saliency attentive model. IEEE Transactions on Image Processing, 27(10), 5142-5154.
[6] Duan, Y., Lv, Y., & Wang, F. Y. (2016). Travel time prediction with LSTM neural network. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems.
[7] Gers, F. (2001). Long short-term memory in recurrent neural networks.
[8] Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing .
[9] Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton university press.
[10] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[11] LeCun, Y. A., Bottou, L., Orr, G. B., & Müller, K. R. (2012). Efficient backprop. Neural networks: Tricks of the trade, 9-48.
[12] McMaster, G. S., & Wilhelm, W. (1997). Growing degree-days: one equation, two interpretations. Agricultural and forest meteorology, 87(4), 291-300.
[13] Miller, P., Lanier, W., & Brandt, S. (2001). Using growing degree days to predict plant stages. Ag/Extension Communications Coordinator, Communications Services, Montana State University-Bozeman, Bozeman, MO, 1-2.
[14] Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In International conference on machine learning.
[15] Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
[16] Sundermeyer, M., Ney, H., & Schlüter, R. (2015). From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 517-529.
[17] Vaswani, et al. (2017). Attention is all you need. In Advances in Neural Information Processing Systems.
[18] Yildirim, Ö. (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in biology and medicine, 96, 189-202.
[19] Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of manufacturing systems, 48, 78-86.