Energy Savings from Optimised In-Field Route Planning for Agricultural Machinery

Various types of sensors technologies, such as machine vision and global positioning system (GPS) have been implemented in navigation of agricultural vehicles. Automated navigation systems have proved the potential for the execution of optimised route plans for field area coverage. This paper presents an assessment of the reduction of the energy requirements derived from the implementation of optimised field area coverage planning. The assessment regards the analysis of the energy requirements and the comparison between the non-optimised and optimised plans for field area coverage in the whole sequence of operations required in two different cropping systems: Miscanthus and Switchgrass production. An algorithmic approach for the simulation of the executed field operations by following both non-optimised and optimised field-work patterns was developed. As a result, the corresponding time requirements were estimated as the basis of the subsequent energy cost analysis. Based on the results, the optimised routes reduce the fuel energy consumption up to 8%, the embodied energy consumption up to 7%, and the total energy consumption from 3% up to 8%.

[1]  Charles L. Peterson,et al.  Agricultural machinery management : a compilation of peer-reviewed articles, standards, engineering practices, data, and terminology , 1988 .

[2]  G. Clarke,et al.  Scheduling of Vehicles from a Central Depot to a Number of Delivery Points , 1964 .

[3]  Jian Jin,et al.  Optimal Coverage Path Planning for Arable Farming on 2D Surfaces , 2010 .

[4]  Dionysis Bochtis,et al.  Coverage planning for capacitated field operations, part II: Optimisation , 2015 .

[5]  Efthymios Rodias,et al.  Model for Energy Analysis of Miscanthus Production and Transportation , 2016 .

[6]  N. Sharma,et al.  Switchgrass production and establishment in the Southern Italy climatic conditions. , 2001 .

[7]  Howard Lee.  Handbook of Bioenergy Crops. A Complete Reference to Species, Development and Applications , 2011 .

[8]  Brian Witney,et al.  Choosing and Using Farm Machines , 1988 .

[9]  Efthymios Rodias,et al.  A Computational Tool for Comparative Energy Cost Analysis of Multiple-Crop Production Systems , 2017 .

[10]  M. Coccia,et al.  Attitudes and Behaviour of Adopters of Technological Innovations in Agricultural Tractors: A Case Study in Italian Agricultural System , 2014 .

[11]  R. Linkolehto,et al.  GNSS error simulator for farm machinery navigation development , 2015, Comput. Electron. Agric..

[12]  Dionysis Bochtis,et al.  Minimising the non-working distance travelled by machines operating in a headland field pattern , 2008 .

[13]  Dionysis Bochtis,et al.  Conceptual model of fleet management in agriculture , 2010 .

[14]  Juan Agüera,et al.  Comparison of positional accuracy between rtk and rtx gnss based on the autonomous agricultural vehicles under field conditions , 2014 .

[15]  Joachim Hertzberg,et al.  Spatio-Temporally Constrained Planning for Cooperative Vehicles in a Harvesting Scenario , 2013, KI - Künstliche Intelligenz.

[16]  Dionysis Bochtis,et al.  Advances in agricultural machinery management: A review , 2014 .

[17]  Alan Renwick,et al.  Measuring and understanding the drivers of agricultural innovation: Evidence from Ireland , 2015 .

[18]  Patrizia Busato,et al.  Agricultural operations planning in fields with multiple obstacle areas , 2014 .

[19]  Hasan Seyyedhasani,et al.  Using the Vehicle Routing Problem to reduce field completion times with multiple machines , 2017, Comput. Electron. Agric..

[20]  Ward Chesworth,et al.  Encyclopedia of soil science. , 2008 .

[21]  Marvin T. Batte,et al.  The economics of precision guidance with auto-boom control for farmer-owned agricultural sprayers , 2006 .

[22]  C. Saunders,et al.  Food miles - comparative energy / emissions performance of New Zealand's agriculture industry , 2006 .

[23]  Dionysis Bochtis,et al.  Benefits from optimal route planning based on B-patterns , 2013 .

[24]  Claus G. Sørensen,et al.  The vehicle routing problem in field logistics: Part II , 2009 .

[25]  Arto Visala,et al.  Coverage path planning algorithms for agricultural field machines , 2009 .

[26]  C. Sonneveld,et al.  Test of an Automatic Precision Guidance System for Cultivation Implements , 1994 .

[27]  Enrico Bonari,et al.  Comparison of Arundo donax L. and Miscanthus x giganteus in a long-term field experiment in Central Italy: Analysis of productive characteristics and energy balance , 2009 .

[28]  Hans W. Griepentrog,et al.  A Mission Planner for an Autonomous Tractor , 2009 .

[29]  Dionysis Bochtis,et al.  Conceptual model of a future farm management information system , 2010 .

[30]  Jian Jin,et al.  Coverage path planning on three‐dimensional terrain for arable farming , 2011, J. Field Robotics.

[31]  Aad Klompe,et al.  Spatial optimisation of cropped swaths and field margins using GIS , 2009 .

[32]  Angela Ribeiro,et al.  Route planning for agricultural tasks: A general approach for fleets of autonomous vehicles in site-specific herbicide applications , 2016, Comput. Electron. Agric..

[33]  O. Kitani.  CIGR Handbook of Agricultural Engineering, Volume V Energy and Biomass Engineering, Chapter 1 Natural Energy and Biomass, Part 1.1 Post-Petroleum Energy and Material , 1999 .

[34]  Christopher J. Atkinson,et al.  Establishing perennial grass energy crops in the UK: A review of current propagation options for Miscanthus , 2009 .