Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 37-47.doi: 10.12133/j.smartag.2020.2.3.202003-SA010

• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles     Next Articles

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

LI Hualong1(), LI Miao1(), ZHAN Kai2(), LIU Xianwang1, YANG Xuanjiang1, HU Zelin1, GUO Panpan1   

  1. 1.Institute of Intelligent Machines, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China
    2.Institute of Animal Husbandry and Veterinary, Agricultural Academy of Anhui Province, Hefei 230031, China
  • Received:2020-03-02 Revised:2020-08-06 Online:2020-09-30 Published:2020-10-26
  • corresponding author: Miao LI,Kai ZHAN E-mail:hlli@iim.ac.cn;mli@iim.ac.cn;zhankai633@126.com


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: