Terrain assessment for precision agriculture using vehicle dynamic modelling

TL;DR

This paper presents a novel terrain classification method for precision agriculture that combines appearance-based and contact-based features, achieving high accuracy in real-field tests to enhance vehicle safety and task efficiency.

Contribution

It introduces a new approach that integrates physics-based contact features with traditional appearance features for improved terrain estimation in agricultural vehicles.

Findings

Contact features alone achieved 85.1% accuracy.

Merged features increased accuracy to 89.1%.

Field experiments validated the effectiveness of the proposed method.

Abstract

Advances in precision agriculture greatly rely on innovative control and sensing technologies that allow service units to increase their level of driving automation while ensuring at the same time high safety standards. This paper deals with automatic terrain estimation and classification that is performed simultaneously by an agricultural vehicle during normal operations. Vehicle mobility and safety, and the successful implementation of important agricultural tasks including seeding, ploughing, fertilising and controlled traffic depend or can be improved by a correct identification of the terrain that is traversed. The novelty of this research lies in that terrain estimation is performed by using not only traditional appearance-based features, that is colour and geometric properties, but also contact-based features, that is measuring physics-based dynamic effects that govern the vehicleeterrain interaction and that greatly affect its mobility. Experimental results obtained from an all-terrain vehicle operating on different surfaces are presented to validate the system in the field. It was shown that a terrain classifier trained with contact features was able to achieve a correct prediction rate of 85.1%, which is comparable or better than that obtained with approaches using traditional feature sets. To further improve the classification performance, all feature sets were merged in an augmented feature space, reaching, for these tests, 89.1% of correct predictions.