Designing a Smart Farming Platform for Sustainable Decision Making

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-2",
							 Title= "Designing a Smart Farming Platform for Sustainable Decision Making", 
							Author= "obias Zimpel, Martin Riekert, Andrea Wild", 
							Doi= "https://doi.org/10.30844/wi_2020_x3-zimpel", 
							 Abstract= "Smart farming platforms (SFP’s) for pig livestock farming are of increasingly relevance to increase sustainable decision making and enhancement of animal welfare. SFPs involve the whole supply chain and integrate various types of data measured, thus enable data-driven solutions using artificial intelligence. While there exists research about SFPs, issues concerning data governance of SFPs are still lacking. Against this backdrop, we develop a SFP for sustainable decision making with respect to data privacy and data security. Our SFP integrates 4 sensor data sources (e.g., temperature control system, and feeding stations), considers farmer characteristics (e.g., projects with pigs), and provides data-driven solutions (e.g., prediction of animal welfare indicators). We report on the current process situation in pig livestock farming as well as on our concept of SFPs for sustainable decision making. We also report on the evaluation of our SFP by validation against defined requirements during the deployment phase.

", 
							 Keywords= "Smart Farming Platform, Livestock farming, Sustainable decision making, Animal welfare, Data Analytics", 
							}
					
obias Zimpel, Martin Riekert, Andrea Wild: Designing a Smart Farming Platform for Sustainable Decision Making. Online: https://doi.org/10.30844/wi_2020_x3-zimpel (Abgerufen 29.03.24)

Abstract

Abstract

Smart farming platforms (SFP’s) for pig livestock farming are of increasingly relevance to increase sustainable decision making and enhancement of animal welfare. SFPs involve the whole supply chain and integrate various types of data measured, thus enable data-driven solutions using artificial intelligence. While there exists research about SFPs, issues concerning data governance of SFPs are still lacking. Against this backdrop, we develop a SFP for sustainable decision making with respect to data privacy and data security. Our SFP integrates 4 sensor data sources (e.g., temperature control system, and feeding stations), considers farmer characteristics (e.g., projects with pigs), and provides data-driven solutions (e.g., prediction of animal welfare indicators). We report on the current process situation in pig livestock farming as well as on our concept of SFPs for sustainable decision making. We also report on the evaluation of our SFP by validation against defined requirements during the deployment phase.

Keywords

Schlüsselwörter

Smart Farming Platform, Livestock farming, Sustainable decision making, Animal welfare, Data Analytics

References

Referenzen

1. BMEL: Nutztierhaltungsstrategie. Zukunftsfähige Tierhaltung in Deutschland. , Berlin (2017).
2. Zapf, R., Schultheiß, U., Knierim, U., Brinkmann, J., Schrader, L.: Assessing farm animal welfare – guidelines for on-farm self-assessment. 72, 214–220 (2017). https://doi.org/10.15150/lt.2017.3166.
3. Schrader, L., Czycholl, I., Krieter, J., Leeb, C., Zapf, R., Ziron, M.: Tierschutzindikatoren: Leitfaden für die Praxis – Schwein. , Darmstadt (2016).
4. Jukan, A., Masip-Bruin, X., Amla, N.: Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review. ACM Comput. Surv. 50, 1–15 (2017).
5. Vranken, E., Berckmans, D.: Precision livestock farming for pigs. Anim. Front. 7, 32– 37 (2017).
6. Matthews, S.G., Miller, A.L., Clapp, J., Plötz, T., Kyriazakis, I.: Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J. 217, 43–51 (2016).
7. Botreau, R., Veissier, I., Butterworth, A., Keeling, L.J., Bracke, M.B.M.: Definition of criteria for overall assessment of animal welfare. Anim. Welf. 16, 225–228 (2007).
8. Manteuffel, G., Schön, P.-C.: Measuring welfare of pigs by automatic monitoring of stress sounds. Meas. Syst. Anim. Data – Bornimer Agrartech. Berichte. 29, 110–118 (2002).
9. Matthews, S.G., Miller, A.L., Plötz, T., Kyriazakis, I.: Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep. 7, 43–51 (2017).
10. Dawkins, M.S.: Animal welfare and efficient farming: is conflict inevitable? Anim. Prod. Sci. 57, 201–208 (2017).
11. Velarde, A., Fàbrega, E., Blanco-Penedo, I., Dalmau, A.: Animal welfare towards sustainability in pork meat production. Meat Sci. 109, 13–17 (2015).
12. Wolfert, S., Ge, L., Verdouw, C., Bogaardta, M.-J.: Big Data in Smart Farming – A review. Agric. Syst. 153, 69–80 (2017).
13. Berckmans, D.: General introduction to precision livestock farming. Anim. Front. 7, 6–11 (2017).
14. Terrasson, G., Villeneuve, E., Pilnière, V., Llaria, A.: Precision Livestock Farming: A Multidisciplinary Paradigm. In: SMART INTERFACES 2017, The Symposium for Empowering and Smart Interfaces in Engineering. pp. 55–59 (2017).
15. Taneja, M., Jalodia, N., Byabazaire, J., Davy, A., Olariu, C.: SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farming. Softw. Pract. Exp. 49, 1055–1078 (2019).
16. Zhang, L., Kim, J., LEE, Y.: The Platform Development of a Real-Time Momentum Data Collection System for Livestock in Wide Grazing Land. Electronics. 7, 71–81 (2018).
17. Ryu, M., Yun, J., Miao, T., Ahn, I.-Y., Choi, S.-C., Kim, J.: Design and Implementation of a Connected Farm for Smart Farming System. In: 2015 IEEE SENSORS (2015).
18. Banhazi, T., Tscharke, M.: Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results*. Aust. J. Multi-Disciplinary Eng. 8, 107–119 (2011).
19. Banhazi, T.: User Friendly Air Quality Monitoring System. Appl. Eng. Agric. 25, 281–290 (2009).
20. Rodriguez, M.A., Cuenca, L., Ortiz, A.: FIWARE Open Source Standard Platform in Smart Farming – A Review. In: PRO-VE 2018: Collaborative Networks of Cognitive Systems. pp. 581–589 (2018).
21. Huang, J., Guo, P., Xie, Q., Meng, X.: Cloud Services Platform based on Big Data Analytics and its Application in Livestock Management and Marketing. In: CloudCom 2016 (2016).
22. Antunes, P., Santos, R., Videira, N.: Participatory decision making for sustainable development—the use of mediated modelling techniques. Land use policy. 23, 44–52 (2006).
23. Ueda, K., Takenaka, T., Váncza, J., Monostori, L.: Value creation and decisionmaking in sustainable society. CIRP Ann. – Manuf. Technol. 58, 681–700 (2009).
24. Ariyachandra, T., Watson, H.J.: Which Data Warehouse Architecture Is Most Successful? Bus. Intell. J. 11, 4–6 (2006).

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito