Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens

ABSTRACT In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main “input” variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs “weekly egg production,” “weekly incubated egg,”, “accumulated commercial egg,” and “viability” showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs.

摘要
近年来，巴西禽蛋产量实现了大幅增长，且过去十年间巴西人均禽蛋消费量显著提升。为降低卫生与财务风险，有关鸡群生产与健康状况的决策必须基于客观标准制定。本研究旨在利用人工神经网络（Artificial Neural Network，ANN）模型，确定商用蛋用种鸡群禽蛋生产性能预测所需的核心"输入"变量。研究采用NeuroShellClassifier与NeuroShell Predictor两款软件构建人工神经网络模型。共选取26项禽蛋生产性状作为输入变量，8项作为输出变量，最终生成包含44120个Excel单元格数据的数据集。模型训练与验证分别采用74.9%与25.1%的数据集。本研究通过多重决定系数（Coefficient of Multiple Determination，R²）分析、均方误差（Mean Squared Error，MSE）计算以及残差图均匀离散性评估，对各人工神经网络模型的预测精度进行了计算与对比。针对"周均禽蛋产量""周均入孵蛋量""累计商品蛋量"以及"鸡群存活率"四个输出变量构建的模型，其R²值均大于0.8；其余模型的R²值均低于0.8。人工神经网络可较好地预测商用蛋用种鸡的8项禽蛋生产性状。该方法可作为禽蛋行业数据管理分析的可选方案，能够量化各输入变量对输出变量的相对贡献度。