An artificial neural network model to predict the effective work time of different agricultural field shapes

Abstract

The aim of this study was to find a model able to extract the net time per unit of net worked area from different agricultural field basic shapes (square, circle, rectangle and triangle) considering the following variables: field gross area, working speed, number of turnings (these depending on the effective working width), side length parallel and orthogonal to working direction, and working direction type. Being this a non-linear problem, an approach based on artificial neural networks is proposed. The model was trained using an artificial dataset calculated for the various shapes (internal test) and then tested on 47 different agricultural operations extracted by a real field dataset for the estimation of the net time (external test). The net time records obtained from both, the trained model and the external test, were correlated and the performance parameter r was extracted. Both regression coefficients (r), for the training and internal test, appear to be excellent being equal to 0.98 with respect to traditional linear approach (0.13). The variable “number of turnings” scored the highest impact, with a value equal to 44.34% for the net time estimation. Finally, the r correlation parameter for the external test resulted to be very high (0.80). This information is very valuable of the use of information management system for precision agriculture.

Keywords

• multivariate statistics
• precision agriculture
• non-linear modelling
• logistics
• agricultural productivity