Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements

Abstract

Establishing and maintaining farmland geometric boundaries is crucial to increasing agricultural productivity. Accurate field boundaries enable farm machinery contractors and other farm stakeholders to calculate charges, costs and to examine machinery performance. Field segmentation is the process by which agricultural field plots are geofenced into their individual field geometric boundaries. This paper presents a novel coordinate-based method to perform trajectory segmentation and field boundary detection from a tractor towing an implement. The main contribution of this research is a practical, robust algorithm which can solve for challenging field-to-field segmentation cases where the operator engages the towed implement continuously across several fields. The algorithm first isolates raw machinery trajectory data into unique job sites by using a coarse filter on geolocation data and implement power-take off activation. Next, the coordinate data is plotted and image processing techniques are applied to erode any pathway(s) that may present in job sites with adjacent working fields. Georeferenced time series tractor and implement data were aggregated from a five-month-long measurement campaign of a silage baling season in Galway, Ireland. The algorithm was validated against two unique machinery implement datasets, which combined, contain a mixture of 296 road-to-field and 31 field-to-field cases. The results demonstrate that the algorithm achieves an accuracy of 100% on a baler implement dataset and 98.84% on a mower implement dataset. The proposed algorithm is deterministic and does not require any additional labor, land traversal or aerial surveillance to produce results with accuracy metrics registering above 98%.