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Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making

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Abstract

With advancements in the mechanisation of sugarcane farming, studies have been fundamental to improving the process—from soil preparation to harvest. Faced with increasing challenges of economic scenarios, alternatives should be sought aimed at optimising resources, reducing costs, improving operational efficiency, logistics, among others. Planting is one of the main agricultural operations, any deviation in this phase harms the crop during the crop cycle, so planning in advance the area to be planted is essential for better results. Analysis of better planting scenarios prior to harvest combined with the use of autopilot requires knowledge of the systematisation areas and skilled labour to guarantee the quality of the process and reduce losses and damages. The objective of this study is to both evaluate and optimise sugarcane planting scenarios based on travel and manoeuvre time, travel distance, number of manoeuvres, and fuel consumption. The study was conducted in the municipality of Tanabi, SP, during the 2013 planting season. The results showed fewer manoeuvres and longer planting lines in the optimised area, increased the availability of the machine and generated possible cost reduction.

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Authors and Affiliations

  1. Department of Engineering and Exact Sciences, Laboratory of Agricultural Machinery and Mechanization, São Paulo State University, Jaboticabal, São Paulo, Brazil

    L. A. S. Nardo, A. R. Gonzaga, L. P. Oliveira & R. P. Silva

  2. University of Sorocaba, Sorocaba, São Paulo, Brazil

    C. S. S. Paixão

  3. Federal University São Carlos, Buri, São Paulo, Brazil

    M. A. Voltarelli

Authors
  1. L. A. S. Nardo
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  2. C. S. S. Paixão
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  3. A. R. Gonzaga
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  4. L. P. Oliveira
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  5. M. A. Voltarelli
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  6. R. P. Silva
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Correspondence to A. R. Gonzaga.

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Nardo, L.A.S., Paixão, C.S.S., Gonzaga, A.R. et al. Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making. Sugar Tech 23, 86–93 (2021). https://doi.org/10.1007/s12355-020-00868-1

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