Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms
Keywords:
cloud-based data management system (CDMS), egg production, intensified laying-hen farms, asynchronous data transmission, metadataAbstract
Management of poultry farms in China mostly relies on manual labor. Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents, making it very difficult for data retrieve, processing and analysis. An integrated cloud-based data management system (CDMS) was proposed in this study, in which the asynchronous data transmission, distributed file system, and wireless network technology were used for information collection, management and sharing in large-scale egg production. The cloud-based platform can provide information technology infrastructures for different farms. The CDMS can also allocate the computing resources and storage space based on demand. A real-time data acquisition software was developed, which allowed farm management staff to submit reports through website or smartphone, enabled digitization of production data. The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center. All the valid historical data of poultry farms can be stored to the remote cloud data center, and then eliminates the need for large server clusters on the farms. Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide. Keywords: cloud-based data management system (CDMS), egg production, intensified laying-hen farms, asynchronous data transmission, metadata DOI: 10.3965/j.ijabe.20160904.2488 Citation: Chen H Q, Xin H W, Teng G H, Meng C Y, Du X D, Mao T T, et al. Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms. Int J Agric & Biol Eng, 2016; 9(4): 106-115.References
[1] Aydin A, Bahr C, Berckmans D. A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis. Computers and Electronics in Agriculture, 2015; 114: 1–6.
[2] Wang C S, Li J X, Huang R L, Lü J X, Li L H. Design and implementation of automatic monitoring system for layers production parameters. Transactions of the CSAE, 2014; 30(16): 181–187. (in Chinese with English abstract)
[3] Zaninelli M, Costa A, Tangorra F M, Rossi L, Agazzi A, Savoini G. Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens. Sensors, 2015; 15(2): 2680–2693.
[4] Sales G T, Green A R, Gates R S, Brown-Brandl T M, Eigenberg R A. Quantifying detection performance of a passive low-frequency RFID system in an environmental preference chamber for laying hens. Computers and Electronics in Agriculture, 2015; 114: 261–268.
[5] McCarthy U, Brennan L, Ward S, Corkery G. Enhanced efficiencies in the poultry industry via real-time monitoring and cloud-enabled tracking. The 6th European Conference on Precision Livestock Farming, 2013; pp.212–222.
[6] Aydin A, Cangar O, Ozcan S E, Berckmans D, Bahr C. Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores. Computers and Electronics in Agriculture, 2010; 73(2): 194–199.
[7] Wang L F, Lu C H, Hu Y N. Implementation of computerized information management network system on 200000 Layer Farm. Transactions of the CSAE, 1999; 15(4): 207–211. (in Chinese with English abstract)
[8] Lu C H, Wu Z M, Wang L F, Hu Y L, Lu Q W, Dou S C, et al. Establishment and application of computerized production management system for large scale poultry farm. Transaction of the CSAE, 2003; 19(6): 256–259. (in Chinese with English abstract)
[9] Yu L, Teng G, Li B, Zhang Y, Lao F, Xing Y. A remote-monitoring system for poultry production management using a 3G-based network. Applied Engineering in Agriculture, 2013; 29(4): 595–601.
[10] Banhazi T M, Babinszky L, Halas V, Tscharke M. Precision Livestock Farming: Precision feeding technologies and sustainable livestock production. Int J Agric & Biol Eng, 2012; 5(4): 54–61.
[11] Berckmans D. Basic principles of PLF: gold standard, labelling and field data. The 6th European Conference on Precision Livestock Farming, 2013; pp.21–29.
[12] Banhazi T M, Lehr H, Black J L, Crabtree H, Schofield P, Tscharke M, et al. Precision livestock farming: An international review of scientific and commercial aspects. Int J Agric & Biol Eng, 2012; 5(3): 1–9
[13] Zhou X Y, Sun Z H, Zhang B L, Yang Y D. A Fast outlier detection algorithm for high dimensional categorical data streams. Journal of Software, 2007; 18(4): 933−942. DOI: 10.1360/jos180933. (in Chinese with English abstract)
[14] Jin C Q, Qian W N, Zhou A Y. Analysis and management of streaming data: A survey. Journal of Software, 2004; 15(8): 1172–1181. (in Chinese with English abstract)
[15] Plimpton S J, Shead T. Streaming data analytics via message passing with application to graph algorithms. Journal of Parallel & Distributed Computing, 2014; 74(8): 2687–2698.
[16] Guo X, Zheng L H, Li M Z, Zhang Y. Intelligent data acquisition and cloud services for apple orchard. Int J Agric & Biol Eng, 2014; 7(2): 146–153.
[17] Weng S J, Gotcher D, Wu H H, Xu Y Y, Yang C W, Lai L S. Cloud image data center for healthcare network in Taiwan. J Med Syst, 2016; 40(4): 89. DOI: 10.1007/s10916-016- 0430-8.
[18] Das K. The Internet of Things: A survey. Computer Networks, 2010; 54(15): 2787–2805.
[19] Kaloxylosa A, Eigenmannc R, Teyed F, Politopouloue Z, Wolfertf S, Shrankg C, et al. Farm management systems and the future internet era. Computers and Electronics in Agriculture, 2012; 89(5): 130–144.
[20] Vlajic N, Stevanovic D, Spanogiannopoulos G. Strategies for improving performance of IEEE 802.15.4/ZigBee WSNs with path-constrained mobile sinks. Computer Communications, 2011; 34(6): 743–757.
[21] Kaloxylosa A, Groumasb A, Sarrisb V, Katsikasb L, Magdalinosb P, Antoniouc E, et al. A cloud-based farm management system: Architecture and implementation. Computers and Electronics in Agriculture, 2014; 100(2): 168–179.
[22] Li K, Deolalikar V, Pradhan N. Big data gathering and mining pipelines for CRM using open-source. IEEE International Conference on Big Data (Big Data), 2015; 2936–2938.
[23] Jia Z C, Hu Z X, Shi X J. Study of remote monitoring and maintenance guiding technique based on LabVIEW for machining centers. Journal of Donghua University, 2005; 22(5): 29–33. (in Chinese with English abstract)
[24] Lao F D, Teng G H, Li J, Yu L G, Li Z. Behavior recognition method for individual laying hen based on computer vision. Transactions of the CSAE, 2012; 28(24): 157–163. (in Chinese with English abstract)
[25] Cao Y F, Chen H Q, Teng G H, Zhao S M, Li Q W. Detection method of laying hens’ vocalization based on power spectral density. Transactions of the CSAM, 2015; 46(2): 276–280. (in Chinese with English abstract)
[26] Chen H Q, Teng G H, Qiu X B, Meng C Y, Cao Y F, Wang C. A real-time monitoring and early warning system based on stream computing for laying hens raise. Transactions of the CSAM, 2016; 47(1): 252–259. (in Chinese with English abstract)
[2] Wang C S, Li J X, Huang R L, Lü J X, Li L H. Design and implementation of automatic monitoring system for layers production parameters. Transactions of the CSAE, 2014; 30(16): 181–187. (in Chinese with English abstract)
[3] Zaninelli M, Costa A, Tangorra F M, Rossi L, Agazzi A, Savoini G. Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens. Sensors, 2015; 15(2): 2680–2693.
[4] Sales G T, Green A R, Gates R S, Brown-Brandl T M, Eigenberg R A. Quantifying detection performance of a passive low-frequency RFID system in an environmental preference chamber for laying hens. Computers and Electronics in Agriculture, 2015; 114: 261–268.
[5] McCarthy U, Brennan L, Ward S, Corkery G. Enhanced efficiencies in the poultry industry via real-time monitoring and cloud-enabled tracking. The 6th European Conference on Precision Livestock Farming, 2013; pp.212–222.
[6] Aydin A, Cangar O, Ozcan S E, Berckmans D, Bahr C. Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores. Computers and Electronics in Agriculture, 2010; 73(2): 194–199.
[7] Wang L F, Lu C H, Hu Y N. Implementation of computerized information management network system on 200000 Layer Farm. Transactions of the CSAE, 1999; 15(4): 207–211. (in Chinese with English abstract)
[8] Lu C H, Wu Z M, Wang L F, Hu Y L, Lu Q W, Dou S C, et al. Establishment and application of computerized production management system for large scale poultry farm. Transaction of the CSAE, 2003; 19(6): 256–259. (in Chinese with English abstract)
[9] Yu L, Teng G, Li B, Zhang Y, Lao F, Xing Y. A remote-monitoring system for poultry production management using a 3G-based network. Applied Engineering in Agriculture, 2013; 29(4): 595–601.
[10] Banhazi T M, Babinszky L, Halas V, Tscharke M. Precision Livestock Farming: Precision feeding technologies and sustainable livestock production. Int J Agric & Biol Eng, 2012; 5(4): 54–61.
[11] Berckmans D. Basic principles of PLF: gold standard, labelling and field data. The 6th European Conference on Precision Livestock Farming, 2013; pp.21–29.
[12] Banhazi T M, Lehr H, Black J L, Crabtree H, Schofield P, Tscharke M, et al. Precision livestock farming: An international review of scientific and commercial aspects. Int J Agric & Biol Eng, 2012; 5(3): 1–9
[13] Zhou X Y, Sun Z H, Zhang B L, Yang Y D. A Fast outlier detection algorithm for high dimensional categorical data streams. Journal of Software, 2007; 18(4): 933−942. DOI: 10.1360/jos180933. (in Chinese with English abstract)
[14] Jin C Q, Qian W N, Zhou A Y. Analysis and management of streaming data: A survey. Journal of Software, 2004; 15(8): 1172–1181. (in Chinese with English abstract)
[15] Plimpton S J, Shead T. Streaming data analytics via message passing with application to graph algorithms. Journal of Parallel & Distributed Computing, 2014; 74(8): 2687–2698.
[16] Guo X, Zheng L H, Li M Z, Zhang Y. Intelligent data acquisition and cloud services for apple orchard. Int J Agric & Biol Eng, 2014; 7(2): 146–153.
[17] Weng S J, Gotcher D, Wu H H, Xu Y Y, Yang C W, Lai L S. Cloud image data center for healthcare network in Taiwan. J Med Syst, 2016; 40(4): 89. DOI: 10.1007/s10916-016- 0430-8.
[18] Das K. The Internet of Things: A survey. Computer Networks, 2010; 54(15): 2787–2805.
[19] Kaloxylosa A, Eigenmannc R, Teyed F, Politopouloue Z, Wolfertf S, Shrankg C, et al. Farm management systems and the future internet era. Computers and Electronics in Agriculture, 2012; 89(5): 130–144.
[20] Vlajic N, Stevanovic D, Spanogiannopoulos G. Strategies for improving performance of IEEE 802.15.4/ZigBee WSNs with path-constrained mobile sinks. Computer Communications, 2011; 34(6): 743–757.
[21] Kaloxylosa A, Groumasb A, Sarrisb V, Katsikasb L, Magdalinosb P, Antoniouc E, et al. A cloud-based farm management system: Architecture and implementation. Computers and Electronics in Agriculture, 2014; 100(2): 168–179.
[22] Li K, Deolalikar V, Pradhan N. Big data gathering and mining pipelines for CRM using open-source. IEEE International Conference on Big Data (Big Data), 2015; 2936–2938.
[23] Jia Z C, Hu Z X, Shi X J. Study of remote monitoring and maintenance guiding technique based on LabVIEW for machining centers. Journal of Donghua University, 2005; 22(5): 29–33. (in Chinese with English abstract)
[24] Lao F D, Teng G H, Li J, Yu L G, Li Z. Behavior recognition method for individual laying hen based on computer vision. Transactions of the CSAE, 2012; 28(24): 157–163. (in Chinese with English abstract)
[25] Cao Y F, Chen H Q, Teng G H, Zhao S M, Li Q W. Detection method of laying hens’ vocalization based on power spectral density. Transactions of the CSAM, 2015; 46(2): 276–280. (in Chinese with English abstract)
[26] Chen H Q, Teng G H, Qiu X B, Meng C Y, Cao Y F, Wang C. A real-time monitoring and early warning system based on stream computing for laying hens raise. Transactions of the CSAM, 2016; 47(1): 252–259. (in Chinese with English abstract)
Downloads
Published
2016-07-31
How to Cite
Hongqian, C., Xin, H., Guanghui, T., Chaoying, M., Xiaodong, D., Taotao, M., & Cheng, W. (2016). Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms. International Journal of Agricultural and Biological Engineering, 9(4), 106–115. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2488
Issue
Section
Information Technology, Sensors and Control Systems
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
