Automatic Identification and Segmentation of Overlapping Fog Droplets Using XGBoost and Image Segmentation

Abstract

Water-sensitive paper (WSP) has been widely used to assess the quality of pesticide sprays. However, fog droplets tend to overlap on WSP. In order to accurately measure the droplet size and grasp the droplet distribution pattern, this study proposes a method based on the optimized XGBoost classification model combined with improved concave-point matching to achieve multi-level overlapping-droplet segmentation. For different types of overlapping droplets, the corresponding improved segmentation algorithm is used to improve the segmentation accuracy. For parallel overlapping droplets, the centre-of-mass segmentation method is used; for non-parallel overlapping droplets, the minimum-distance segmentation method is used; and for strong overlapping of a single concave point, the vertical-linkage segmentation method is used. Complex overlapping droplets were gradually segmented by loop iteration until a single droplet was obtained or no further segmentation was possible, and then ellipse fitting was used to obtain the final single-droplet profile. Up to 105 WSPs were obtained in an orchard field through drone spraying experiments, and were used to validate the effectiveness of the method. The experimental results show that the classification model proposed in this paper achieves an average accuracy of 98% in identifying overlapping-droplet types, which effectively meets the needs of subsequent segmentation. The overall segmentation accuracy of the method is 91.35%, which is significantly better than the contour-solidity and watershed-based algorithm (76.19%) and the improved-concave-point-segmentation algorithm (68.82%). In conclusion, the method proposed in this paper provides an efficient and accurate new approach for pesticide spraying quality assessment.

Keywords

• image recognition
• image segmentation
• concave-point matching
• XGBoost