| Autor: |
Gomez-Vazquez A; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86280, Tabasco, Mexico., Tırınk C; Department of Animal Science, Faculty of Agriculture, Igdir University, TR76000 Igdir, Turkey., Cruz-Tamayo AA; Facultad de Ciencias Agropecuarias, Universidad Autónoma de Campeche, Escárcega C.P. 24350, Campeche, Mexico., Cruz-Hernandez A; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86280, Tabasco, Mexico., Camacho-Pérez E; Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes s/n, Mérida C.P. 97302, Yucatán, Mexico., Okuyucu İC; Department of Animal Science, Faculty of Agriculture, Ondokuz Mayis University, TR55139 Samsun, Turkey., Şahin HA; Research Institute of Hemp, Ondokuz Mayis University, TR55139 Samsun, Turkey., Dzib-Cauich DA; Tecnológico Nacional de México, Instituto Tecnológico Superior de Calkiní, Av. Ah-Canul, Calkiní C.P. 24900, Campeche, Mexico., Gülboy Ö; Department of Animal Science, Faculty of Agriculture, Ondokuz Mayis University, TR55139 Samsun, Turkey., Garcia-Herrera RA; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86280, Tabasco, Mexico., Chay-Canul AJ; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86280, Tabasco, Mexico. |
| Abstrakt: |
This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R 2 , RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R 2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science. |
