An ensemble machine learning approach for the prediction of body weight of chickens from body measurement

This study aimed to develop an ensemble Machine learning (ML) model based on K-Nearest Neighbor (KNN), Random Forest (RF), Regression Tree (RT) and Support Vector Machine (SVM) for the prediction of body weight (BW) of chickens from their morphometric traits. The data of 100 Ross 308 broiler chickens (50 female and 50 male) from day 1 to 29 were used for predicting the BW of chickens using various body measurements such as body girth, body length, keel length, wing length and shank length. The data were randomly partitioned into training (80%) and testing (20%) datasets and 10-fold cross-validation was employed to check the stability of the model. The predictive performance of the proposed ensemble method was evaluated and compared with individual ML models using evaluation criteria of adjusted coefficient of determination (), root mean square error (), mean absolute error ( and mean absolute percentage error . The proposed ensemble model outperformed all other ML methods used in the study, having very high predictive accuracy with  (0.999, 0.999),  (3.222, 5.465),  (2.332, 3.913) and  (0.941, 2.029) values for training and testing datasets, respectively. The results of the study revealed that the proposed ensemble model may help researchers and practitioners to accurately predict the BW of chickens from body measurements.