An Internet of Things-Based Cluster System for Monitoring Lactating Sows' Feed and Water Intake
文献类型： 外文期刊
作者： He, Xinyuan 1 ; Zeng, Zhixiong 1 ; Liu, Yanhua 1 ; Lyu, Enli 1 ; Xia, Jingjing 1 ; Wang, Feiren 4 ; Luo, Yizhi 3 ;
作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
2.Guangdong Lab Lingnan Modern Agr, Maoming Branch, Guangzhou 510642, Peoples R China
3.State Key Lab Swine & Poultry Breeding Ind, Guangzhou 510645, Peoples R China
4.Guangdong Mech & Elect Polytech, Sch Automobile, Guangzhou 510550, Peoples R China
5.Guangzhou Jiaen Technol Co Ltd, Guangzhou 510642, Peoples R China
6.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China
关键词： lactating sow; intelligent feeding; cluster system; IoT platform
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.3； 五年影响因子：3.5 ）
ISSN：
年卷期： 2024 年 14 卷 6 期
页码：
收录情况： SCI
摘要： 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.
- 相关文献
作者其他论文 更多>>
-
Distribution Characteristics and Prediction of Temperature and Relative Humidity in a South China Greenhouse
作者：Wei, Xinyu;Guo, Jiaming;Dong, Zhaojie;Lu, Enli;Liu, Yanhua;Wei, Xinyu;Li, Bin;Lu, Huazhong;Guo, Jiaming;Lu, Enli;Liu, Yanhua;Yang, Fengxi
关键词：temperature; relative humidity; ventilation; cooling efficiency; Venlo greenhouse
-
A deep learning-based approach for fully automated segmentation and quantitative analysis of muscle fibers in pig skeletal muscle
作者：Yao, Zekai;Li, Hao;Li, Xinxin;Li, Jianhao;Luo, Yizhi;Meng, Fanming;Yao, Zekai;Zheng, Enqin;Yang, Jie;Li, Hao;Li, Xinxin;Wang, Ting;Fan, Zhenfei;Zhan, Yuexin;Yang, Yingshan;Wu, Zhenfang;Yao, Zekai;Zheng, Enqin;Yang, Jie;Li, Hao;Li, Xinxin;Wang, Ting;Fan, Zhenfei;Zhan, Yuexin;Yang, Yingshan;Wu, Zhenfang;Wo, Jingjie;Yin, Ling;Wu, Zhenfang;Luo, Yizhi;Zheng, Enqin;Yang, Jie;Wu, Zhenfang
关键词：Pigs; Skeletal muscle; Deep learning; Image segmentation; Quantitative analysis
-
Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals
作者：Luo, Yizhi;Luo, Haowen;Luo, Yizhi;Lu, Huazhong;Luo, Haowen;Lv, Enli;Li, Bin;Meng, Fanming;Xia, Jinjin;Lv, Enli;Zeng, Zhixiong;Meng, Fanming;Yang, Aqing
关键词：nursery pigs; feeding behavior recognition; functional area proposals; behavioral quantification; transformer
-
An Improved Rotating Box Detection Model for Litchi Detection in Natural Dense Orchards
作者：Li, Bin;Lu, Huazhong;Wei, Xinyu;Zhou, Xingxing;Luo, Yizhi;Guan, Shixuan;Zhang, Zhenyu
关键词：litchi detection; oriented bounding box; transformer module; eca attention mechanism; small target detection
-
Enhanced Pest Recognition Using Multi-Task Deep Learning with the Discriminative Attention Multi-Network
作者：Dong, Zhaojie;Guo, Jiaming;Zeng, Zhixiong;Wei, Xinyu;Wu, Yonglin
关键词：deep learning; pest recognition; multi-task learning
-
Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy
作者：Li, Peng;Liu, Huaming;Han, Liguo;Li, Chuanzong;Luo, Yizhi;Li, Peng;Liu, Huaming;Han, Liguo;Li, Chuanzong;Jin, Qingting
关键词：Fruit; Nondestructive detection; Machine learning; Chemometrics; Spectral analysis
-
A multi-omics view of the preservation effect on Camellia sinensis leaves during low temperature postharvest transportation
作者：Chen, Jiahao;Zheng, Peng;Sun, Binmei;Mei, Shuang;Guo, Jiaming;Zeng, Zhixiong;Mei, Shuang;Lu, Huazhong
关键词：Widely targeted secondary metabolomics; Transcriptome; Refrigerated transportation; Postharvest tea leaves; Flavonoids
