Dairy Product Production Prediction Based on BiLSTM-Attention model
DOI:
https://doi.org/10.71204/by8g5g40Keywords:
Dairy Product Yield, Yield Prediction, BiLSTM, Attention, LSTMAbstract
With the rapid development of the dairy industry, accurate prediction of production is crucial to optimizing production plans. To this end, this paper proposes a prediction model that combines a bidirectional long short-term memory network (BiLSTM) with an attention mechanism (Attention). BiLSTM can effectively capture long-term and short-term dependencies in time series, while the attention mechanism can dynamically focus on the features of key time points, thereby improving prediction accuracy. Experiments show that the BiLSTM-Attention model improves the accuracy of dairy production prediction compared to traditional regression analysis and a single LSTM model, especially when processing long time series data. This study provides an effective solution for the accurate prediction of dairy production.
References
Aslan, M. F., Unlersen, M. F., Sabanci, K., & Durdu, A. (2021). CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, 98, 106912.
Başarslan, M. S. (2025). MC &M-BL: a novel classification model for brain tumor classification: multi-CNN and multi-BiLSTM. The Journal of Supercomputing, 81(3), 1-25.
Chen, T., Xu, R., He, Y., et al. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221-230.
Deshmukh, S. S., & Paramasivam, R. (2016). Forecasting of milk production in India with ARIMA and VAR time series models. Asian Journal of Dairy and Food Research, 35(1), 17-22.
Gao, Y., Tian, M., Grana, D., Xu, Z., & Xu, H. (2025). Attention mechanism‐assisted recurrent neural network for well log lithology classification. Geophysical Prospecting, 73(2), 628-649.
Kavianpour, P., Kavianpour, M., Jahani, E., & Ramezani, A. (2023). A CNN-BiLSTM model with attention mechanism for earthquake prediction. The Journal of Supercomputing, 79(17), 19194-19226.
Khan, S., Muhammad, Y., Jadoon, I., Awan, S. E., & Raja, M. A. Z. (2025). Leveraging LSTM-SMI and ARIMA architecture for robust wind power plant forecasting. Applied Soft Computing, 170, 112765.
Li, Y., Zhu, Z., Kong, D., Han, H., & Zhao, Y. (2019). EA-LSTM: Evolutionary attention-based LSTM for time series prediction. Knowledge-Based Systems, 181, 104785.
Liu, G., & Guo, J. (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325-338.
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753.
Murphy, M. D., O’Mahony, M. J., Shalloo, L., French, P., & Upton, J. (2014). Comparison of modelling techniques for milk-production forecasting. Journal of dairy science, 97(6), 3352-3363.
Nguyen, Q. T., Fouchereau, R., Frenod, E, et al. (2020). Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture, 170, 105258.
Qiang, G. (2025). Scalability improvement and empirical analysis of PBFT consensus mechanism in blockchain. Electronic Components and Information Technology, 9(01),192-195.
Sanjulián, L., Fernández-Rico, S., González-Rodríguez, N., et al. (2025). The Role of Dairy in Human Nutrition: Myths and Realities. Nutrients, 17(4), 646.
Shan, L., Liu, Y., Tang, M., et al. (2021). CNN-BiLSTM hybrid neural networks with attention mechanism for well log prediction. Journal of Petroleum Science and Engineering, 205, 108838.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
Vithitsoontorn, C., & Chongstitvatana, P. (2022). Demand forecasting in production planning for dairy products using machine learning and statistical method. In 2022 international electrical engineering congress (iEECON), 1-4.
Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7, 51522-51532.
Yuan, F., Huang, X., Zheng, L., et al. (2025). The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains. Information, 16(4), 268.
Zanchi, M., La Porta, C. A., et al. (2025). Influence of microclimatic conditions on dairy production in an Automatic Milking System: Trends and Time-Series Mixer predictions. Computers and Electronics in Agriculture, 229, 109730.
Zhang, J., Ye, L., & Lai, Y. (2023). Stock price prediction using CNN-BiLSTM-Attention model. Mathematics, 11(9), 1985.
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