Monitoring and modeling temperature variations inside silage stacks using novel wireless sensor networks

Computers and Electronics in Agriculture

Volumn 69, Issue 2, 2009, Pages 149-157

  Green, Ole ^a   Nadimi, Esmaeil S ^b   Blanes Vidal, Victoria ^b   Jørgensen, Rasmus N ^b   Storm, Ida M L Drejer ^c   Sørensen, Claus G ^a  

^a AARHUS UNIVERSITY   (Denmark)

^b UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^c TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Decomposition; Silage; System identification; Temperature; Wireless sensor networks (WSN)

Indexed keywords

BLACK-BOX MODEL; BLACK-BOX MODELING; BOX-JENKINS; BOX-JENKINS MODELS; CHEMICAL DAMAGES; COMMUNICATION PROTOCOLS; DECOMPOSITION PROCESS; FREQUENCY RANGES; HANDSHAKING; HIGH LOAD; IDENTIFICATION METHOD; MEASURED DATA; MODEL ORDER; MODEL TEMPERATURES; NETWORK CHARACTERISTICS; PREDICTION ACCURACY; PREDICTION ERROR METHOD; RELIABLE PERFORMANCE; SILAGE; SOIL TEMPERATURE; SYSTEM IDENTIFICATION; SYSTEM IDENTIFICATION TECHNIQUES; TEMPERATURE PREDICTION; TEMPERATURE VARIATION; THIRD-ORDER; TRANSMISSION POWER; WIRELESS NODES; WIRELESS SENSOR NODE;

COMMUNICATION; DAMAGE DETECTION; ELECTRIC NETWORK SYNTHESIS; GATEWAYS (COMPUTER NETWORKS); GEOLOGIC MODELS; HOUSING; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; MONITORING; NETWORK PROTOCOLS; SENSOR NETWORKS; SENSOR NODES; TELECOMMUNICATION EQUIPMENT; TEMPERATURE DISTRIBUTION;

WIRELESS SENSOR NETWORKS;

COMMUNICATION NETWORK; DATA ACQUISITION; ERROR ANALYSIS; MEASUREMENT METHOD; NUMERICAL MODEL; SENSOR; SILAGE; TEMPERATURE EFFECT;

EID: 70349443395     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2009.07.021     Document Type: Article

References (44)

1. Ashbell, Weinberg Z.G., Hen Y., and Filya I. The effects of temperature on the aerobic stability of wheat and corn silages. J. Ind. Microbiol. Biotechnol. 28 (2002) 261-263
2. Barnett, N.G., Shinners, K.J., 1998. Analysis of systems to measure mass flow rate and moisture on a forage harvester. ASAE Paper No. 981118. American Society of Agricultural Engineers, St. Joseph, MI.
3. Bauer D., Deistler M., and Scherrer W. Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs. Automatica 35 (1999) 1243-1254
4. Bauer D., and Jansson M. Analysis of the asymptotic properties of the MOESP type of subspace algorithms. Automatica 36 4 (2000) 497-509
5. Blanes-Vidal V., Guijarro E., Balasch S., and Torres A.G. Application of computational fluid dynamics to the prediction of airflow in a mechanically ventilated commercial poultry building. Biosyst. Eng. 100 1 (2008) 105-116
6. ® system: fieldbus for integrated management of extensive areas of irrigated land. Microprocess. Microsyst. 25 (2001) 177-184
7. Deistler D., Peternell K., and Scherrer. Consistency and relative efficiency of subspace methods. Automatica 31 (1995) 1865-1875
8. Elkaim, G., 2002. System Identification for Precision Control of a Wing Sailed GPS-Guided Catamaran. Ph.D. Thesis. Department of Aeronautics and Astronautics, Stanford University.
9. Forssell U., and Ljung L. Closed-loop identification revisited. Automatica 35 (1999) 1215-1241
10. Gevers M. A personal view on the development of system identification. 13th IFAC Symp. on System Identification (SYSID 2003). Rotterdam, The Netherlands, August 27-29, 2003 (2003) 773-784 (Publié)
11. Jansson M., and Wahlberg B. On consistency of subspace methods for system identification. Automatica 34 12 (1998) 1507-1519
12. Juang J.N. Applied System Identification (1988), Prentice-Hall PTR. 0-13-079211-X
13. Kim R.G.E., Iversen W., and Pierce F.J. Instrumentation and control for wireless sensor network for automated irrigation. Presented at the 2006 ASABE Annual International Meeting, Paper no. 061105. Portland, OR (2006)
14. Knudsen T. Consistency analysis of subspace identification methods based on a linear regression approach. Automatica 37 (2001) 81-89
15. Laursen C.H. Automatic data acquisition from feed mixer wagons in dairy production. 23rd NJF Congress (2005)
16. Larimore W.E. System identification, reduced-order filtering and modeling via canonical variate analysis. In: Rao H.S., and Dorato T. (Eds). Proc. 1983 American Control Conf. (1983), IEEE, New York
17. Lawrence K.C., Funk D.B., and Windham W.R. Parallel-plate moisture sensor for yellow-dent field corn. Trans. ASAE 42 5 (1999) 1353-1357
18. Lewis F.L. In: Cook D.J., and Das S.K. (Eds). Wireless Sensor Networks. Smart Environments: Technologies, Protocols and Applications (2004), John Wiley, New York. 978-0-471-54448-7
19. Ljung L., and McKelvey T. Subspace identification from closed loop data. Signal Process. 52 (1996) 209-215
20. Ljung L. System identification: theory for the user (1999), Prentice-Hall. 0-13-656695-2
21. Marra F., and Romano V. A mathematical model to study the influence of wireless temperature sensor during assessment of canned food sterilization. J. Food Eng. 59 2003 (2002) 245-252
22. Martel H., and Savoie P. Sensors to measure mass-flow-rate through a forage harvester. Can. Agric. Eng. 42 3 (2000) 123-129
23. McDonald, Henderson N., and Heron S. The Biochemistry of Silage. 2nd edn. (1991), Chalcombe Publications, Marlow, UK
24. McDonald P., Edwards R.A., Greenhalgh J.F.D., and Morgan C.A. Animal Nutrition. 6th edn. (2002), Pearson, Harlow, UK pp. 521-522
25. Nadimi E.S., Søgaaard H.T., Bak T., and Oudshoorn F.W.I. ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass. Comput. Electron. Agric. 61 May (2) (2008) 79-87
26. Nadimi E.S., Søgaaard H.T., and Bak T. ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosyst. Eng. 100 2 (2008) 167-176
27. Nadimi E.S., and Søgaard H.T. Observer Kalman filter identification and multiple-model adaptive estimation technique for classifying animal behavior using wireless sensor networks. Comput. Electron. Agric. 68 1 (2009) 9-17
28. Norton T., and Sun D. Computational fluid dynamics (CFD)-an effective and efficient design and analysis tool for the food industry: a review. Trends Food Sci. Technol. (2006) 600-620
29. Norton T., Sun D., Grant J., Fallon R., and Dodd V. Applications of computational fluid dynamics (CFD) in the modelling and design of ventilation systems in the agricultural industry: a review. Bioresource Technol. 98 (2007) 2386-2414
30. Osman P.S., Grenier D., and Thériault R. Parallel-plate capacitance moisture sensor for hay and forage. Presented at the 2002 ASAE Annual international Meeting/CIGR XV World Congress, Paper no. 021055. Chicago, IL (2002)
31. 2-abhängige Veränderungen der Mikroflora in Silagen mit Lactobakterienzusatz (Oxygen dependend changes of the microflora in silages with addition of a Lactobacillus inoculant). Landwirt. Forsch. 37 (1984) (Kongreßband)
32. Peternell K., Scherrer W., and Deistler M. Statistical analysis of novel subspace identification methods. Signal Process. 52 (1996) 161-177
33. Pippard C.J., Porter M.G., Steen R.W.J., Gordon F.J., Mayne S., Poots E., Unsworth E.F., and Kilpatrick D.J. A method for obtaining and storing uniform silage for feeding experiments. Anim. Feed Sci. Technol. 57 (1996) 87-95
34. Schumann, Miller W.M., Zaman Q.U., Hostler K.H., Buchanon S., and Cugati S. Variable rate granular fertilization of citrus groves: spreader performance with single-tree prescription zones. Appl. Eng. Agric. 22 1 (2006) 19-24
35. Savoie, P., Lemire, P., Thériault, R., 2000. Evaluation of five sensors to estimate grass throughput in a forage harvester. ASAE Paper No. 001124. American Society of Agricultural Engineers, St. Joseph, MI.
36. Shinners, K.J., Barnett, N.G., Schlesser, W.M., 2000. Measuring mass-flow-rate and moisture on a large square baler. ASAE Paper No. 001037. American Society of Agricultural Engineers, St. Joseph, MI.
37. Snell, H.G.J., Oberndorfer, C., Lücke, W., 2000. Electromagnetic measurement of the moisture content of chopped maize. ASAE Paper No. 003003. American Society of Agricultural Engineers, St. Joseph, MI.
38. Snell H.G.J., Van den Weghe H.F.A., and Lücke W. Continuous Measurement of the Oxygen Concentration during Ensiling-Technology and Preliminary Results. Written for Presentation at the 2001 ASAE Annual International Meeting. California, USA, July 30-August 1 (2001)
39. Tiano A., Sutton R., Lozowicki A., and Naeem W. Observer Kalman filter identification of an autonomous underwater vehicle. Control Eng. Practice 15 6 (2007) 727-739
40. Van Overschee P., and De Moor B. N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30 (1994) 75-93
41. Verhaegen M. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica 30 (1994) 61-74
42. Vivoni E.R., and Camilli R. Real-time streaming of enviromental field data. Comput. Geosci. 29 (2003) 457-468
43. Wang, Zhang N., and Wang M. Wireless sensors in agriculture and food industry: recent development and future perspective. Comput. Electron. Agric. 50 (2006) 1-14
44. Williams A.G., Hoxey R.P., and Lowe J.F. Changes in temperature and silo gas composition during ensiling storage and feeding-out grass silage. Grass Forage Sci. (1997) 176-189