Multi-view density-based field-road classification for agricultural machinery: DBSCAN and object detection

Field-road classification that automatically identifies the operation modes (either in-field or on-road) of GNSS (Global Navigation Satellite System) points plays an important role for the operational performance analysis of agricultural vehicles. Intuitively, a field often has high point density because in-field driving speed is rather low and the distance between consecutive strips is closed. In this paper, two methods were used to capture the in-field high-density characteristic: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and an object detection model. DBSCAN is a widely-used density-based clustering algorithm, which clusters the points with high point density into a cluster. Alternatively, a trajectory can be transformed into an image, and an object detection model can be applied to detect objects in the image, where an object is a set of pixels with high pixel density (i.e., a set of points with high point density). Finally, the two field-mad classification results are combined using DBI (Davis Bouldin index), a metric which can evaluate the quality of either classification result. The developed method was validated by the harvesting trajectories of two crops (wheat and paddy), and the densitybased field-road classification achieved 85.97% and 73.34% accuracy on the wheat data and the paddy data, respectively.

Automated summary

Field-road classification that automatically identifies the operation modes of GNSS points plays an important role in the analysis of agricultural vehicles. This study proposed two methods to capture the high-density characteristic in field driving: DBSCAN and an object detection model. The two classification results were combined using the DBI metric. Experimental results showed high accuracy in the field-road classification for wheat and paddy harvesting trajectories.