Big Data for Farm Machines: An Algorithm for Estimating Tractors’ Operating Costs

Abstract

Estimating the operating costs of agricultural tractors is key to the decision-making process of any farm, helping to assess the feasibility of investments in machinery and thus the impact on agricultural profitability.

However, the variation of several parameters over the years, such as environmental conditions, technological innovations, field capacity and the performance of agricultural machinery, have now led to a different impact on these indicators.

The aim is to provide new tools for the precise estimation of tractor operating costs, based on the information gathered from machines via CANBUS interfaces and geospatial data processing. To this end, GPS data from the machines were collected and combined with the European Copernicus digital elevation models (DEM) in order to supplement the dataset with information on altitude, distance and average speed.

As a result, after data aggregation, the algorithm was able to determine working times, fuel consumption and areas worked for the open field operations examined. All information could be reproduced on geographic information systems (GIS) as well as terrestrial browsers.

These parameters might provide a detailed estimate of the operating costs of any farm. Further developments may include maintenance alerts, analysis of equipment performance and even an additional classification of other agricultural activities (ploughing, mowing, pesticide distribution, fertilisation, etc.).