1. Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making
- Author
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Carla Segatto Strini Paixão, Luan Pereira de Oliveira, Murilo Aparecido Voltarelli, Rouverson Pereira da Silva, A. R. Gonzaga, L. A. S. Nardo, Universidade Estadual Paulista (Unesp), Univ Sorocaba, and Universidade Federal de São Carlos (UFSCar)
- Subjects
0106 biological sciences ,Agricultural planning ,Precision agriculture ,Computer science ,business.industry ,Process (engineering) ,media_common.quotation_subject ,Sowing ,04 agricultural and veterinary sciences ,Agricultural engineering ,01 natural sciences ,Running time ,AgroCAD(R) ,Cost reduction ,Agriculture ,040103 agronomy & agriculture ,Fuel efficiency ,Damages ,0401 agriculture, forestry, and fisheries ,Operational efficiency ,Quality (business) ,business ,Agronomy and Crop Science ,010606 plant biology & botany ,media_common - Abstract
Made available in DSpace on 2020-12-10T17:39:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-08-06 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. Sao Paulo State Univ, Dept Engn & Exact Sci, Lab Agr Machinery & Mechanizat, Sao Paulo, Brazil Univ Sorocaba, Sao Paulo, Brazil Univ Fed Sao Carlos, Sao Paulo, Brazil Sao Paulo State Univ, Dept Engn & Exact Sci, Lab Agr Machinery & Mechanizat, Sao Paulo, Brazil
- Published
- 2020