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

Yunus Emre Gür

Makale detayları

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

Yazarlar:

Yunus Emre Gür

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Zootechny (Other)

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.8 Issue 2, pp.327-346

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315302

pISSN:

2618-5946

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Yunus Emre Gür

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Zootechny (Other)

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.8 Issue 2, pp.327-346

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315302

pISSN:

2618-5946

Deep Learning

Machine Learning

LSTM

Cow Cheese Production

Futurue Prediction

Sayı

Makale detayları

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

Yazarlar: Yunus Emre Gür

Yayıncı: Gültekin ÖZDEMİR

Ana Başlıklar: Zootechny (Other)

Dergi: International Journal of Agriculture Environment and Food Sciences Vol.8 Issue 2, pp.327-346

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001315302

pISSN: 2618-5946

Deep Learning

Machine Learning

LSTM

Cow Cheese Production

Futurue Prediction

Sayı

Yazarlar:

Yunus Emre Gür

Yayıncı:

Gültekin ÖZDEMİR

Ana Başlıklar:

Zootechny (Other)

Dergi:

International Journal of Agriculture Environment and Food Sciences Vol.8 Issue 2, pp.327-346

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315302

pISSN:

2618-5946