Auto-Steering and Controlled Traffic Farming – Route Planning and Economics

Sørensen, Claus G., Rodias, Efthymios and Bochtis, Dionysis (2017) Auto-Steering and Controlled Traffic Farming – Route Planning and Economics. In: Precision Agriculture: Technology and Economic Perspectives. Springer, pp. 129-145. ISBN UNSPECIFIED

Full content URL: http://doi.org/10.1007/978-3-319-68715-5_6

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Item Type:Book Section
Item Status:Live Archive

Abstract

Agriculture nowadays includes automation systems that contribute significantly to many levels of the food production process. Such systems include GPS based systems like auto-steering and Controlled Traffic Farming (CTF). These systems have led to many innovations in agricultural field area coverage design. Integrating these advancements, two different route planning designs, a traditional and an optimised one, are outlined and explained in this chapter. Four different machinery scenarios were tested in four fields each, and the main aim was to compare the two different route planning systems under economic criteria and identify the best operational route coverage design criterion. The results show that there are significant reductions in operational costs varying from 9 to 20%, depending on the specific machinery and field configurations. Such results show the considerable potential of advanced route planning designs and further optimization measures. They indicate the need for research efforts that quantify the operational and economic benefits by optimising field coverage designs in the headlands, turnings or obstacles avoidance according to the actual configuration to minimize the non-working activities and, as a consequence, the overall operational cost.

Keywords:Route planning, CTF
Subjects:H Engineering > H650 Systems Engineering
G Mathematical and Computer Sciences > G200 Operational Research
D Veterinary Sciences, Agriculture and related subjects > D470 Agricultural Technology
D Veterinary Sciences, Agriculture and related subjects > D471 Agricultural Machinery
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:39232
Deposited On:23 Dec 2019 10:13

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