Innovation in the dairy industry: forecasting cow cheese production with machine learning and deep learning models

Yıl 2024, Cilt: 8 Sayı: 2, 327 - 346, 27.06.2024

Yunus Emre Gür

https://doi.org/10.31015/jaefs.2024.2.9

Öz

This study focuses on the use of deep learning and machine learning models to forecast cow cheese production in Turkey. In particular, our research utilizes the LSTM (long short-term memory) model to forecast cow cheese production for the next 12 months by extensively utilizing deep learning and machine learning techniques that have not been applied in this field before. In addition to LSTM, models such as GRU (Gated Recurrent Unit), MLP (Multi-Layer Perceptron), SVR (Support Vector Regression), and KNN (K-Nearest Neighbors) were also tested, and their performances were compared using RMSE (Root Mean Square Error), MSE (Mean Squared Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and (Coefficient of Determination) metrics. The findings revealed that the LSTM model performed significantly better than the other models in terms of RMSE, MSE, MAE, and MAPE values. This result indicates that the LSTM model provides high accuracy and reliability in forecasting cow cheese production. This achievement of the model offers important applications in areas such as supply chain management, inventory optimization, and demand forecasting in the dairy industry.

Anahtar Kelimeler

Deep Learning, Machine Learning, LSTM, Cow Cheese Production, Futurue Prediction

Kaynakça

• Agarap, A. F. M. (2018). A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. In Proceedings of the 2018 10th international conference on machine learning and computing (pp. 26-30).
• Akay, M. F. & Abasıkeleş, I. (2010). Predicting the performance measures of an optical distributed shared memory multiprocessor by using support vector regression. Expert Systems with Applications, 37(9), 6293-6301.
• Athiwaratkun, B. & Stokes, J. W. (2017). Malware classification with LSTM and GRU language models and a character-level CNN. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 2482-2486).
• Bulut, E. (2024). Market Volatility and Models for Forecasting Volatility. In Business Continuity Management and Resilience: Theories, Models, and Processes (pp. 220-248). IGI Global.
• Desai, M. & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 4, 1-11.
• Durlu-Özkaya, F. & Gün, İ. (2007). Cheese culture in Anatolia. ICANAS, International Congress of Asian and North African Studies, 10-15.
• Gandotra, S., Chhikara, R. & Dhull, A. (2023). Wheat, Rice and Corn Yield Prediction for Jammu District Using Machine Learning Techniques. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 499-512). Singapore: Springer Nature Singapore.
• Goyal, S. & Goyal, G. (2013). Machine learning models for predicting shelf life of processed cheese. International Journal of Open Information Technologies, 1(7), 28-31.
• Güllü, M. (2022). The Chemistry Of Milk: Cheese And Cheese Varieties In Turkey. Thematic Researches in the Field of Gastronomy I, 47.
• Hsu, S. H., Hsieh, J. P. A., Chih, T. C. & Hsu, K. C. (2009). A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression. Expert Systems with Applications, 36(4), 7947-7951.
• Kahraman, E. M. (2012). Milk and Dairy Products Production and Consumption Comparative Analysis of Turkey and World Data. Agricultural Engineering, (359), 48-52.
• Li, B., Lin, Y., Yu, W., Wilson, D. I. & Young, B. R. (2021a). Application of mechanistic modelling and machine learning for cream cheese fermentation pH prediction. Journal of Chemical Technology & Biotechnology, 96(1), 125-133.
• Li, W., Kiaghadi, A. & Dawson, C. (2021b). Exploring the best sequence LSTM modeling architecture for flood prediction. Neural Computing and Applications, 33, 5571-5580.
• Li, X. & Liu, J. (2023), Hyperspectral Imaging Combined with Long-Short Term Memory Network for Accurately Detecting Adulteration in Milk. Available at SSRN 4597631.
• Liseune A, Poel V, Hut P, Eerdenburg F & Hostens M (2021). Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning, Computers and Electronics in Agriculture, no. 191.
• Ma, Y., Zhang, Z., Kang, Y. & Özdoğan, M. (2021). Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sensing of Environment, 259, 112408.
• Nosouhian, S., Nosouhian, F. & Kazemi Khoshouei, A. (2021). A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU. Preprints. https://doi.org/10.20944/preprints202107.0252.v1
• Patwary, M. M. A., Satish, N. R., Sundaram, N., Liu, J., Sadowski, P. Racah, E., ... & Dubey, P. (2016). Panda: Extreme scale parallel k-nearest neighbor on distributed architectures. In 2016 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 494-503). IEEE.
• Qian, Z. L., Juan, D. C., Bogdan, P., Tsui, C. Y., Marculescu, D. & Marculescu, R. (2015). A support vector regression (SVR)-based latency model for network-on-chip (NoC) architectures. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35(3), 471-484.
• Ramchoun, H., Idrissi, M. J., Ghanou, Y. & Ettaouil, M. (2017). Multilayer Perceptron: Architecture Optimization and training with mixed activation functions. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (pp. 1-6).
• Smagulova, K. & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. The European Physical Journal Special Topics, 228(10), 2313-2324.
• Şimşek, A. I. (2024). Using Machine Learning and Deep Learning Methods in Predicting the Islamic Index Price. Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques). IGI Global. Doi:10.4018/979-8-3693-1038-0.ch018
• Turkish Statistical Institute, https:// data.tuik.gov.tr/ Access Date: 21.09.2023
• Yıldırım, A. & Altunç, Ö. F. (2020). Estimation of milk production in Muş province with arima model. Anemon Muş Alparslan University Journal of Social Sciences, 8(UMS'20), 137-146.
• Yu, C. D., Huang, J., Austin, W., Xiao, B. & Biros, G. (2015, November). Performance optimization for the k-nearest neighbors kernel on x86 architectures. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1-12).