Cut-edge detection method for wheat harvesting based on stereo vision

A cut-edge detection method for wheat based on stereo vision was proposed in this work to obtain the navigation path of a combine harvester. First, the point cloud was acquired by the stereo camera. The crop area was extracted with the threshold obtained by the Otsu method. Then, the point cloud of the crop area was gridded. The grids were clustered by the density-based spatial clustering of applications with noise method to classify the different crop areas. After filtering the noise caused by the ridge in the yield, the grids of interest were extracted. The edge point was extracted in each grid of interest. The polynomial fitting method was then used to acquire the straight or curved cut-edge. A total of 300 images were selected for the test of crop area extraction and crop areas classification. The results showed that the success rate of crop area extraction was 93.7% and the success rate of crop areas classification was 91.1%. 100 images were selected to extract the edge points and compare with the true value of manual measurement. Experiment results showed that the average deviation of the edge points was 8.47 cm, the maximum deviation was 23.1 cm, and the standard deviation was 5.97 cm. The proposed method is thus capable of providing support for the automatic navigation of combine harvesters.

Automated summary

This paper proposes a cut-edge detection method for wheat based on stereo vision to obtain the navigation path of a combine harvester. By extracting point cloud, thresholding, and gridding, crop areas can be successfully identified and classified. The proposed method achieves high success rates and good accuracy in extracting cut-edges.