An Internet of Things-Based Cluster System for Monitoring Lactating Sows’ Feed and Water Intake

Xinyuan He, Zhixiong Zeng, Yanhua Liu, Enli Lyu (), Jingjing Xia, Feiren Wang and Yizhi Luo
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Xinyuan He: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhixiong Zeng: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yanhua Liu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Enli Lyu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jingjing Xia: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Feiren Wang: Schools of Automobile, Guangdong Mechanical and Electrical Polytechnic, Guangzhou 510550, China
Yizhi Luo: State Key Laboratory of Swine and Poultry Breeding Industry, Guangzhou 510645, China

Agriculture, 2024, vol. 14, issue 6, 1-20

Abstract: Acquiring real-time feeding information for monitoring lactating sows and their feeding requirements is a challenging task. Real-time data represent an important input for numerous tasks, such as disease monitoring, nutritional regulation, and feeding modeling. However, concurrently monitoring large numbers of sows and processing the real-time information for modeling is challenging using existing platforms. In this paper, we describe the design and development of a system that monitors and processes sows’ feed and water consumption in real time. The system was custom-developed using open-source networking technologies. The system consists of three components: an electronic sow feeder connected to a central controller via a CAN network, an MQTT service cluster, and a data processing program. The MQTT service cluster uses Netty to develop a single service node, and it uses Zookeeper and Redis to complete node registration, discovery, and scheduling. The data processing program is based on Spark and Flink. We conducted comparative testing of three common codecs (Java Serializer, Marshalling, and Protostuff) to further speed up data transmission. The results of the experiment show that, with three service nodes, the system can concurrently monitor up to 20,000 sows. Moreover, the system achieves optimal performance when monitoring 10,000 sows at the same time, with a TPS of 6399 pcs/s and an RT of 643 ms.

Keywords: lactating sow; intelligent feeding; cluster system; IoT platform (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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