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

Mulin Liu, Hongxi Chen (), Zhenyu Zhou, Xiaodong Du, Yuxiao Zhao, Hengyi Ji and Guanghui Teng ()
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Mulin Liu: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Hongxi Chen: Information Office (Network Technology Center), China Agricultural University, Beijing 100083, China
Zhenyu Zhou: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Xiaodong Du: CRRC Industrial Institute (Qingdao) Co., Ltd., Qingdao 266000, China
Yuxiao Zhao: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Hengyi Ji: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Guanghui Teng: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 8, 1-22

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.

Keywords: livestock breeding; Internet of Things; real-time monitoring (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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