17 Farhat Iqbal Maha Urooj Zil-E-Huma Journal of Animal and Plant Sciences JAPS 20232023/08/08 33 4 794-804 1018-7081 https://doi.org/10.36899/JAPS.2023.4.0673 <p>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&nbsp;<em>10</em>-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 (<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image001.png" alt="" width="36" height="17">), root mean square error (<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image002.png" alt="" width="36" height="17">), mean absolute error (<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image003.png" alt="" width="34" height="17">&nbsp;and mean absolute percentage error&nbsp;<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image004.png" alt="" width="48" height="17">. The proposed ensemble model outperformed all other ML methods used in the study, having very high predictive accuracy with&nbsp;<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image001.png" alt="" width="36" height="17">&nbsp;(0.999, 0.999),&nbsp;<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image002.png" alt="" width="36" height="17">&nbsp;(3.222, 5.465),&nbsp;<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image005.png" alt="" width="29" height="17">&nbsp;(2.332, 3.913) and&nbsp;<img src="https://archives.thejaps.org.pk/Volume/2023/33-04/07-2022-JAPS-236%20Ok_files/image006.png" alt="" width="37" height="17"> (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.</p> Body weight, chickens, morphological traits, machine learning, ensemble method Pakistan Agricultural Scientists Forum https://thejaps.org.pk/AbstractView.aspx?mid=2022-JAPS-236