Reducing fuel consumption in weed and pest control using robotic tractors

Computers and Electronics in Agriculture

Volumn 114, Issue , 2015, Pages 96-113

  Gonzalez de Soto, Mariano ^a,b   Emmi, Luis ^a   Garcia, Isaias ^b   Gonzalez de Santos, Pablo ^a  

^a CENTRE FOR AUTOMATION AND ROBOTICS   (Spain)

^b UNIVERSITY OF LEÓN   (Spain)

Author keywords

Atmospheric emissions; Digital elevation model (DEM); Energy efficiency; Path planning; Reducing fuel consumption

Indexed keywords

AGRICULTURE; AIR POLLUTION; ATMOSPHERIC SPECTRA; CROPS; ENERGY EFFICIENCY; FIRE RESISTANCE; FLAME RESISTANCE; FRICTION; FUELS; GRAVITATION; MOTION PLANNING; PEST CONTROL; POLLUTION; POTENTIAL ENERGY; SURVEYING; TRACTORS (AGRICULTURAL); TRACTORS (TRUCK); WEED CONTROL;

AGRICULTURAL VEHICLES; ATMOSPHERIC EMISSION; ATMOSPHERIC POLLUTANT EMISSIONS; ATMOSPHERIC POLLUTANTS; AUTOMATED APPLICATIONS; DIFFERENT TREATMENTS; DIGITAL ELEVATION MODEL; RESISTANCE COEFFICIENTS;

ROBOTICS;

AGRICULTURAL TECHNOLOGY; ATMOSPHERIC POLLUTION; DIGITAL ELEVATION MODEL; ENERGY EFFICIENCY; FUEL CONSUMPTION; HERB; PEST CONTROL; PIXEL; REAL TIME; ROBOTICS; SUSTAINABILITY; TERRAIN; WEED CONTROL;

EID: 84927645294     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.04.003     Document Type: Article

References (31)

1. ANSI/ASAE S296.4 DEC95 Agricultural Machinery Management Data, 1995.In: ASAE Standards. St. Josep, Michigan, pp. 118-120.
2. ASAE D497.4 FEB2003 Agricultural Machinery Management Data, 2003. In: ASAE Standards. St. Josep, Michigan, pp. 273-280.
3. ASAE D497.7 MAR2011 Agricultural Machinery Management Data, 2011. In: ASAE Standards. St. Josep, Michigan, pp. 372-380.
4. ASAE S313.3 FEB04 Soil Cone Penetrometer, 2004. In: ASAE Standards. St. Josep, Michigan, pp. 903-904.
5. Bochtis D.D., Sørensen C.G. The vehicle routing problem in field logistics Part I. Biosyst. Eng. 2009, 104:447-457. 10.1016/j.biosystemseng.2009.09.003.
6. Bochtis D.D., Vougioukas S.G. Minimising the non-working distance travelled by machines operating in a headland field pattern. Biosyst. Eng. 2008, 101:1-12. 10.1016/j.biosystemseng.2008.06.008.
7. Bochtis D.D., Sørensen C.G., Green O. A DSS for planning of soil-sensitive field operations. Decision Support Syst. 2012, 53:66-75. 10.1016/j.dss.2011.12.005.
8. Carballido, J., Perez-Ruiz, M., Gliever, C., Agüera, M., 2012. Design, development and lab evaluation of a weed control sprayer to be used in robotic systems, In: Proceedings of the First International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture. Presented at the Applications of automated systems and robotics for crop protection in sustainable precision agriculture, Pisa University Press srl, Pisa, Italy, pp. 23-29.
9. CNH America LLC, 2009. BOOMER 3040, 3045, 3050 CVT Service Manual Complete Contents.
10. EOSDIS. 2014. Nasa's Earth Observing System Data Information System [WWW Document], n.d. EOSDIS NASAs Earth Obs. Syst (accessed 18.07.13). http://reverb.echo.nasa.gov.
11. España, Real Decreto 61/2006, de 31 de enero, por el que se determinan las especificaciones de gasolinas, gasóleos, fuelóleos y gases licuados del petróleo y se regula el uso de determinados biocarburantes, 2006, Boletín Oficial del Estado, 17 de febrero de 2006, núm. 41, pp. 6342-6357.
12. Frasconi, C., Martelloni, L., Fontanelli, M., Raffaelli, M., Emmi, L., Pirchio, M., Peruzzi, A., 2014. Design and full realization of physical weed control (PWC) automatic machine within the RHEA project, in: Second International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and Forestry. Presented at the New trends in mobile robotics, perception and actuation for agriculture and forestry, PGM, Madrid, Spain, pp. 3-11.
13. Grečenko A. Predicting the performance of wheel tractors in combination with implements. J. Agric. Eng. Res. 1968, 13:49-63. 10.1016/0021-8634(68)90120-0.
14. Grisso R., Pitman R., Perumpral J.V., Vaughan D., Roberson G.T., Hoy R.M. "Gear Up and Throttle Down" to Save Fuel 2001, Virginia Cooperative Extension.
15. Grisso R., Kocher M., Vaughan D. Predicting tractor fuel consumption. Biol. Syst. Eng. Pap. Publ. 2004.
16. Grisso, R.D., Vaughan, D.H., Roberson, G.T., 2006. Method for Fuel Prediction for Specific Tractor Models. In: 2006 ASAE Annual Meeting.
17. Grogan J., Morris D.A., Searcy S.W., Stout B.A. Microcomputer-based tractor performance monitoring and optimization system. J. Agric. Eng. Res. 1987, 38:227-243. 10.1016/0021-8634(87)90091-6.
18. Hameed I.A., Bochtis D.D., Sørensen C.G. Driving angle and track sequence optimization for operational path planning using genetic algorithms. Appl. Eng. Agric. 2011, 27:294-306.
19. Hameed I.A., Bochtis D.D., Sørensen C.G., Jensen A.L., Larsen R. Optimized driving direction based on a three-dimensional field representation. Comput. Electron. Agric. 2013, 91:145-153. 10.1016/j.compag.2012.12.009.
20. Harris H.D. Prediction of the torque and optimum operating point of diesel engines using engine speed and fuel consumption. J. Agric. Eng. Res. 1992, 53:93-101. 10.1016/0021-8634(92)80076-5.
21. Harris H.D., Pearce F. A universal mathematical model of diesel engine performance. J. Agric. Eng. Res. 1990, 47:165-176. 10.1016/0021-8634(90)80038-V.
22. International Energy Agency, 2013. Oil Information: Documentation For Beyond 2020 Files.
23. Jin J., Tang L. Optimal coverage path planning for arable farming on 2D surfaces. Trans. ASABE 2010, 53:283-295.
24. Jin J., Tang L. Coverage path planning on three-dimensional terrain for arable farming. J. Field Robot. 2011, 28:424-440. 10.1002/rob.20388.
25. Larsen W.E. Efficiency of utilization of four-wheel drive tractors. Agric. Energy ASAE Publ. 1981, 81:417-421.
26. Mileusnić Z.I., Petrović D.V., Dević M.S. Comparison of tillage systems according to fuel consumption. Energy 2010, 35:221-228. 10.1016/j.energy.2009.09.012.
27. Oksanen T., Visala A. Coverage path planning algorithms for agricultural field machines. J. Field Robot. 2009, 26:651-668. 10.1002/rob.20300.
28. Rahimi-Ajdadi F., Abbaspour-Gilandeh Y. Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement 2011, 44:2104-2111. 10.1016/j.measurement.2011.08.006.
29. RHEA. 2014. RHEA Project - EU [WWW Document], n.d. "Robot Fleets Highly Eff. Agric. For. Manag. (accessed 30.10.13). http://www.rhea-project.eu/.
30. Sarri, D., Lisci, R., Rimediotti, M., Vieri, M., 2014. RHEA Airblast Sprayer: Calibration Indexes of the Airjet Vector Related to Canopy and Foliage Characteristics, In: Second International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and Forestry. Presented at the New Trends in Mobile Robotics, Perception and Actuation for Agriculture and Forestry, PGM, Madrid, Spain, pp. 73-81.
31. Titan Enterprises Ltd - Titan flow meters for oil, boiler and diesel fuel applications - PD400 oil flowmeter [WWW Document], 2014. URL (accessed 20.02.14). http://www.flowmeters.co.uk/.