Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations

Abstract

Egg production rate and egg weight are core indicators for evaluating the production performance of broiler breeders. The accurate prediction of these indicators can significantly enhance farm economic efficiency and can provide a basis for future production strategies. Currently, there is a lack of research on the application of machine learning (ML) models to predict egg production rate and egg weight in broiler breeders. In this study, we collected data on age, feed intake, water consumption, and environmental factors (temperature, humidity and wind speed) from three poultry houses to train the predictive models. Based on this data, we developed three different datasets. In each dataset, data from a single poultry house were divided into a training set and a validation set in an 8:2 ratio, and data from the remaining two poultry houses were combined to form the test set. We systematically compared the performances of the following seven ML models in predicting egg production rate and egg weight: random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), least squares support vector machine (LSSVM), k-nearest neighbors (kNN), XGBoost, and LightGBM. The results indicated that the XGBoost model demonstrated the best performance across all three datasets. In predicting egg production rate, the XGBoost model achieved a mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of less than 2.86%, 4.17% and 7.03%, respectively. For egg weight predictions, the XGBoost model's MAE, RMSE and MAPE were less than 0.63g, 0.86g and 1.1%, respectively. Given the inherent black-box nature of ML models, we used the Shapley additive explanations (SHAP) method to interpret the key features influencing the XGBoost model's predictions and the interactions between these features. The key features for predicting egg production rate are age, feed intake and effective temperature (ET). For egg weight prediction, the most important features are age, wind speed, temperature-humidity index (THI) and ET. This approach enhanced the model's transparency and credibility. This study provides scientific evidence for predicting the production performance of broiler breeders. Accurately predicting egg production rate and egg weight provides a scientific basis for farm operations, aiding in optimizing resource allocation, improving production efficiency, enhancing animal welfare, and ultimately boosting the farm's profitability.

Keywords: Broiler breeder; Egg production rate; Egg weight; Machine learning.

Fig. 1

Schematic of the sensor layout in the chicken house.

Fig. 2

Pearson correlation heatmap of features in dataset of house 1.

Fig. 3

Pearson correlation heatmap of features in dataset of house 2.

Fig. 4

Pearson correlation heatmap of features in dataset of house 3

Fig. 5

Workflow of the research. Note: MAE, RMSE and MAPE represent mean absolute error, root mean squared error and mean absolute percentage error. SHAP represents Shapley additive explanations.

Fig. 6

Summary plot of the mean absolute SHAP values of the XGBoost model for egg weight prediction.

Fig. 7

SHAP global explanation on XGBoost model for egg weight prediction.

Fig. 8

SHAP main and interaction effect plots of age in predicting egg weight: (a) age, (b) age and V_mean, (c) age and THI_mean, and (d) age and ET_mean.

Fig. 9

Summary plot of mean absolute SHAP values of XGBoost model for egg production rate prediction.

Fig. 10

SHAP global explanation on the XGBoost model for egg production rate prediction.

Fig. 11

SHAP main and interaction effect plots of age in predicting egg production rate: (a) age, (b) age and feed intake, (c) age and ET_mean, and (d) age and V_max.