Development of an Intelligent Service Platform for a Poultry House Facility Environment Based on the Internet of Things

Author:

Liu Mulin1ORCID,Chen Hongxi2,Zhou Zhenyu1,Du Xiaodong3,Zhao Yuxiao1,Ji Hengyi1ORCID,Teng Guanghui1

Affiliation:

1. College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China

2. Information Office (Network Technology Center), China Agricultural University, Beijing 100083, China

3. CRRC Industrial Institute (Qingdao) Co., Ltd., Qingdao 266000, China

Abstract

In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm’s data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform’s operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics.

Funder

National Key R&D Program of China

Publisher

MDPI AG

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