Construction Method and Performance Test of Prediction Model for Laying Hen Breeding Environmental Quality Evaluation

doi:10.12133/j.smartag.2020.2.3.202003-SA010

Abstract

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

CLC Number:

TP183

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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质量评价等级	温度/℃	湿度/％	光照强度/lx	NH₃浓度/（mg?m^-3)
5级（优）	20~25	60~70	28~30	<15
4级（良）	18~20或25~26	55~60或70~75	20~28或30~35	15~17
3级（一般）	14~18或26~27	50~55或75~78	15~20或35~40	17~18
2级（差）	10~14或27~30	40~50或78~80	10~15或40~50	18~25
1级（极差）	<10或>30	<40或>80	<10或>50	>25

试验号	MAE	MAPE	R²
1	0.9113	0.0181	0.8116
2	0.9120	0.0175	0.8324
3	0.9131	0.0178	0.8231
4	0.9253	0.0188	0.7980
5	0.8929	0.0172	0.8449
6	0.9011	0.0177	0.8246
7	0.8744	0.0169	0.8564
8	0.8913	0.0171	0.8392
9	0.7913	0.0153	0.8793
10	0.9103	0.0186	0.8160
平均值	0.8923	0.0175	0.8326

试验号	MAE	MAPE	R²
1	0.9394	0.0184	0.8300
2	0.8091	0.0159	0.8616
3	0.8714	0.0171	0.8559
4	0.7825	0.0155	0.8869
5	0.9020	0.0177	0.8261
6	0.8913	0.0170	0.8416
7	0.9124	0.0175	0.8430
8	0.9129	0.0179	0.8364
9	0.9031	0.0180	0.8551
10	0.7710	0.0151	0.8796
平均值	0.8695	0.0170	0.8512

指标	BP	GA-BP	PSO-BP	改进CS-BP
MAE	0.1701	0.1100	0.0913	0.0865
MAPE	0.0421	0.0301	0.0165	0.0159
R²	0.7125	0.7911	0.8316	0.8569

环境质量等级	样本总数/个	分类正确样本数/个	分类错误样本数/个	准确率
1	60	57	3	0.9500
2	60	56	4	0.9333
3	60	57	3	0.9500
4	60	58	2	0.9667
5	60	56	4	0.9333