First Results in Vision-Based Crop Line Tracking

Abstract

Automation of agricultural harvesting equipment in the near term appears both economically viable and technically feasible. This paper describes a vision-based algorithm which guides a harvester by tracking the line between cut and uncut crop. Using this algorithm, a harvester has successfully cut roughly one acre of crop to date, at speeds of up to 4.5 miles an hour in an actual alfalfa field. A broad range of methods for detecting the crop cut boundary were considered, including both range-based and vision-based techniques; several of these methods were implemented and evaluated on data from an alfalfa field. The final crop-line detection algorithm is presented, which operates by computing the best-fit step function of a normalized-color measure of each row of an RGB image. Results of the algorithm on some sample crop images are shown, and potential improvements are discussed.