蛋鸡设施养殖环境质量评价预测模型构建方法及性能测试

doi:10.12133/j.smartag.2020.2.3.202003-SA010

摘要/Abstract

摘要：

蛋鸡设施养殖环境质量对蛋鸡的健康生长和生产性能的提升至关重要。蛋鸡养殖环境是多环境因子相互影响制约的复杂非线性系统，凭借单一的养殖环境参数难以对环境质量做出准确有效的评价。针对上述问题，本研究综合蛋鸡设施养殖环境的温度、湿度、光照强度、氨气浓度等多个环境影响因子，在布谷鸟搜索算法优化神经网络（CS-BP）预测模型的基础上，构建了改进的CS-BP的蛋鸡设施养殖环境质量评价预测模型。将构建的改进CS-BP预测模型与BP神经网络、遗传算法优化BP神经网络（GA-BP）、粒子群算法优化BP神经网络（PSO-BP）3种深度学习方法进行性能参数分析比对，结果表明：改进CS-BP评价预测模型的平均绝对误差（MAE）、平均相对误差（MAPE）和决定系数（R²）分别为0.0865、0.0159和0.8569，其各项指标性能均优于上述3种对比模型，该模型具有较强的模型泛化能力和较高的预测精度。对改进CS-BP的蛋鸡设施养殖环境质量评价模型进行测试，其分类准确率达0.9333以上。本研究构建的模型可以为蛋鸡设施养殖环境质量提供更加全面有效的科学评价，对实现蛋鸡生产环境的最优控制，促进蛋鸡生产性能的提升具有重要意义。

关键词: 蛋鸡设施养殖, 环境质量评价, 布谷鸟搜索算法优化神经网络（CS-BP）, 遗传算法优化BP神经网络（GA-BP）, 粒子群算法优化BP神经网络（PSO-BP）, 深度学习, 多环境因子

Abstract:

Environmental quality of facilities affects the healthy growth and production of laying hens. The breeding environment of laying hens is a complex and non-linear system in which multiple environmental factors interact and restrict each other. It is difficult to make an accurate and effective evaluation on the suitability of laying hens with a single breeding environment parameter. In order to solve the above problem, an improved cuckoo search algorithm optimization neural network (CS-BP) model for the evaluation and prediction of the environmental suitability of laying hen facility was proposed in this research. In this model, the effects of environmental factors such as temperature, humidity, light intensity and ammonia concentration were comprehensively analyzed, and the problem of high prediction accuracy caused by BP neural network easily falling into local minimum value was overcome. In the experiment, the model was compared with BP neural network, genetic algorithm optimized BP neural network (GA-BP) and particle swarm optimization BP neural network (PSO-BP). The results showed that the mean absolute error (MAE), mean relative error (MAPE) and the coefficient of determination (R²) of the prediction model based on the improved CS-BP were 0.0865, 0.0159 and 0.8569, respectively. The prediction model based on the improved CS-BP had a strong generalization ability and a high testing precision, and its index performance was better than the above three comparison models. The classification accuracy of the improved CS-BP model was tested, and the result was 0.9333. The model constructed in this research can provide more comprehensive and effective scientific evaluation for the environmental quality of laying hens facility, which is of great significance to realize the optimal control of the production environment and promote the production performance of layers.

Key words: facility breeding for laying hens, environmental quality evaluation, CS-BP, GA-BP, PSO-BP, deep learning, multiple environmental factors

中图分类号:

TP183

李华龙, 李淼, 詹凯, 刘先旺, 杨选将, 胡泽林, 郭盼盼. 蛋鸡设施养殖环境质量评价预测模型构建方法及性能测试[J]. 智慧农业（中英文）, 2020, 2(3): 37-47.

LI Hualong, LI Miao, ZHAN Kai, LIU Xianwang, YANG Xuanjiang, HU Zelin, GUO Panpan. Construction Method and Performance Test of Prediction Model for Laying Hen Breeding Environmental Quality Evaluation[J]. Smart Agriculture, 2020, 2(3): 37-47.

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