The Realization of Pig Intelligent Feeding Equipment and Network Service Platform

  • Weihong MaEmail author
  • Jinwei Fan
  • Chunjiang Zhao
  • Huarui Wu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)


Proper feeding of pigs can increase the litter size and improve the disease resistance level. In recent years, intelligent and automatic equipment, which can collect feeding times, feed intake, feed time and growth conditions, have been applied to the pig feeding. Most equipment can feed both manually and automatically. Not enough attention has been paid to one pig’s health condition, living environment, and dietary status, which should be considered together in order to make an accurate decision on the feed intake of each pig. At the same time, there are not many network service platforms in China which can effectively manage the intelligent and automatic equipment remotely and simultaneously. To improve pigs’ productivity and enhance the intelligent management of pigs, wireless sensor network, intelligent sensors, network service platform, and reasoning and decision-making technology have been utilized in the management of pigs in multiple areas throughout China. Single feed intake, living environment information, fitness, and weight for pigs throughout China with different conditions were collected in the network service platform by using the intelligent feed equipment which had several different sensors. Meanwhile, the network service platform could recognize the identity of each pig and provide accurate feed remotely. The network service platform would send a text message or an audible and visual alarm to inform the pig keeper whether the pig’s feed intake was proper. According to the reasoning and decision-making model we built in the network service platform, we can remotely obtain through the platform more accurate information within seconds as to each pig’s feeding status. Moreover, the experiment showed that the feeding container was the key factor that influenced the precision of feeding, and the measured value was closely approximate to the target value with error correction.


Management of pigs Internet of things Monitoring and warning Reasoning and decision-making Network service platform 



This work is partially funded by the National Natural Science Foundation of China, (project number F010407). We thank Dr. Tina Giannoukos for her English-language editing.


  1. 1.
    Hall, A.D., Hill, W.G., Bampton, P.R.: Genetic and phenotypic parameter estimates for feeding pattern and performance test traits in pigs. J. Anim. Sci. 68(1), 43–48 (1999)Google Scholar
  2. 2.
    Maselyne, J., Saeys, W., Van, N.A.: Review: quantifying animal feeding behaviour with a focus on pigs. J. Physiol. Behav. 138, 37–51 (2015)CrossRefGoogle Scholar
  3. 3.
    Latruffe, L., Balcombe, K., Davidova, S.: Technical and scale efficiency of crop and livestock farms in Poland: does specialization matter? J. Agric. Econ. 32(3), 281–296 (2015)CrossRefGoogle Scholar
  4. 4.
    Pomar, J., Pomar, C.: A knowledge-based decision support system to improve sow farm productivity. Expert Syst. Appl. 29(1), 33–40 (2005)CrossRefGoogle Scholar
  5. 5.
    Botta, A., Donato, W.D., Persico, V.: Integration of cloud computing and internet of things. J. Future Gen. Comput. Syst. 56(C), 684–700 (2016)Google Scholar
  6. 6.
    Li, S., Xu, L.D., Zhao, S.: The internet of things: a survey. J. Inf. Syst. Front. 17(2), 243–259 (2015)CrossRefGoogle Scholar
  7. 7.
    Yerunkar, M., Rangnekar, A., Reshamwala, A.: Implementing methodologies for achieving a communication channel between a mobile phone and a remote computer. J. Int. J. Comput. Sci. Inf. Technol. Secur. 2(2), 293–298 (2012)Google Scholar
  8. 8.
    Wang, Y.J.: A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation. J. Appl. Soft Comput. J. 30(C), 412–420 (2015)CrossRefGoogle Scholar
  9. 9.
    Timmer, B., Olthuis, W., Berg, A.V.D.: Ammonia sensors and their applications—a review. J. Sens. Actuators B Chem. 107(2), 666–677 (2005)CrossRefGoogle Scholar
  10. 10.
    Olson, K.R.: Hydrogen sulfide as an oxygen sensor. J. Antioxid. Redox Signal. 22(5), 377–397 (2015)CrossRefGoogle Scholar
  11. 11.
    De Souza, K.G., Woodward, S., Fare, J.W.D., Schott, S.H.: Check fraud detection process using checks having radio frequency identifier (RFID) tags and a system therefor. US, US20040000987 (2004)Google Scholar
  12. 12.
    Yoo, W.J., et al.: Infrared fiber-optic sensor for non-contact temperature measurements. In: 3rd International Conference on Sensing Technology, pp. 500–503 (2008)Google Scholar
  13. 13.
    Tong, A., Newman, J.A., Martin, A.H., Fredeen, H.T.: Live animal ultrasonic measurements of subcutaneous fat thickness as predictors of beef carcass composition. J. Can. Vet. J. La Revue Veterinaire Canadienne 61(2), 483–491 (1981)CrossRefGoogle Scholar
  14. 14.
    Wang, Z.: The influence of environmental temperature and humidity on the body temperature and water content of chorthippus dubius (zub). Acta Entomologica Sinica (1989)Google Scholar
  15. 15.
    Puskala, T.: System and method for transmission of predefined messages among wireless terminals accessing an on-line service, and a wireless terminal. US, US20020165024 (2002)Google Scholar
  16. 16.
    Liu, B., Zhang, X., Ren, X.: Wireless data transmission between iOS client and web server. In: International Conference on Computer Science & Education, pp. 351–354. IEEE (2014)Google Scholar
  17. 17.
    Hansen, S.T., Hauberg, S., Hansen, L.K.: Data-driven forward model inference for EEG brain imaging. J. Neuroimage 139, 249–258 (2016)CrossRefGoogle Scholar
  18. 18.
    Penny, W.D., Zeidman, P., Burgess, N.: Forward and backward inference in spatial cognition. J. Plos Comput. Biol. 9(12), e1003383 (2013)CrossRefGoogle Scholar
  19. 19.
    Kato, Y., Narita, M., Akiguchi, C.: The network service platform for real-world data. In: International Conference on Advanced Information Networking and Applications Workshops, pp. 55–60. IEEE Computer Society (2009)Google Scholar
  20. 20.
    Norton, B.: Eclipse as a development platform for semantic web services. Eclipse Technology Exchange (2004)Google Scholar
  21. 21.
    Gosling, James: The Java Language Specification. China Machine Press, Beijing (2006)Google Scholar
  22. 22.
    Gosling, J., Joy, B., Steele, G., Bracha, G.: The Java Language Specification, 3 edn. (2005). J. Java, 14(2–3), 133–158Google Scholar
  23. 23.
    Linksvayer, T., Mikheyev, A.: Data tables from MySQL database for gene expression analysis. J. Dev. 130(25), 6221–6231 (2015)Google Scholar
  24. 24.
    Bell, C., Kindahl, M., Thalmann, L.: MySQL High Availability: Tools for Building Robust Data Centers. O’Reilly Media Inc., Sebastopol (2010)Google Scholar
  25. 25.
    Chen, X.Y.: Study and realization of uncertain reasoning machine base on expert system. Manuf. Autom. 33, 78–80 (2011)Google Scholar
  26. 26.
    Liu, H.M., Chen, X.Y.: The study and realization of an uncertain reasoning machine in the expert system platform. J. Nanyang Inst. Technol. 2, 17–20 (2010)Google Scholar
  27. 27.
    Zou, Y., Finin, T., Chen, H.: F-OWL: an inference engine for semantic web. In: Hinchey, M.G., Rash, J.L., Truszkowski, W.F., Rouff, C.A. (eds.) FAABS 2004. LNCS, vol. 3228, pp. 238–248. Springer, Heidelberg (2004)Google Scholar
  28. 28.
    Su, H., Wen, Z., Wu, Z.: Study on an intelligent inference engine in early-warning system of dam health. J. Water Resour. Manag. 25(6), 1545–1563 (2011)CrossRefGoogle Scholar
  29. 29.
    Zhang, S., Zhang, J., Zhang, C.: EDUA: an efficient algorithm for dynamic database mining. J. Inf. Sci. 177(13), 2756–2767 (2007)CrossRefGoogle Scholar
  30. 30.
    Tzafestas, S., Palios, L., Cholin, F.: Diagnostic expert system inference engine based on the certainty factors model. J. Knowl.-Based Syst. 7(1), 17–26 (1994)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Weihong Ma
    • 1
    • 2
    • 3
    • 4
    Email author
  • Jinwei Fan
    • 1
  • Chunjiang Zhao
    • 2
    • 3
    • 4
  • Huarui Wu
    • 2
    • 3
    • 4
  1. 1.College of Mechanical Engineering and Applied Electronics TechnologyBeijing University of TechnologyBeijingChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Key Laboratory of Agri-InformaticsMinistry of AgricultureBeijingChina
  4. 4.Beijing Engineering Research Center of Agriculture Internet of ThingsBeijingChina

Personalised recommendations