## 3 Results and Analysis
### Overlapping-Droplet-Segmentation-Test Validation and Analysis  
In order to verify the effectiveness of the proposed segmentation method, we compared it with existing state-of-the-art methods in the field of fog droplet segmentation, which are based on contour solidity and watershed algorithms [30; 31] and improved-concave-point-segmentation algorithms [32; 33]. Figure 13 shows the segmentation and extraction results of overlapping droplets by different methods. The results show that the contour-based solidity and watershed algorithm can effectively improve the relative error of the traditional distance-transform-watershed-segmentation algorithm and reduce the phenomenon of over-segmentation, but it is difficult to correctly deal with severely overlapping fog droplets, and it still causes damage to the key details of the image information, and the integrity of the image cannot be guaranteed. The existing concave-point-segmentation algorithm is better for ensuring the integrity of the fog droplet image, and the segmentation of the fog droplets with a lesser degree of overlap is good, but it is not effective in dealing with multiple overlapping droplets, and it is almost impossible to segment the strongly overlapping droplets that contain only one concave point. Figure 11: Confusion matrix based on improved XGBoost experimental results. Figure 12: Changes in performance metrics of XGBoost model before and after optimisation. For single concave strongly overlapping and parallel overlapping droplets, both algorithms have difficulty in finding accurate segmentation points, and the problem of inability to segment or inaccurate segmentation occurs. Even for parallel overlapping droplets with obvious concave features, the improved concave-point algorithm can segment correctly in some cases, but the extracted contour accuracy is not high. In the case of non-parallel overlapping, the two methods perform relatively well. The contour solidity and watershed-based algorithm is suitable for processing droplets with low overlap. However, it is still difficult to generate heavily overlapping segmentation points. The accuracy of the improved-concave-point-segmentation algorithm is improved, but the concave-point-detection accuracy is low and it is difficult to cope with complex overlapping situations. Figure 13: Segmentation effect of different types of overlapping droplets. Where (**a**) is the original overlapping droplet on WSP; (**b**) shows the segmentation results of the proposed method in this paper; (**c**) presents the result of segmentation based on contour solidity and watershed algorithm; and (**d**) shows the segmentation results using the classic concave-point-matching algorithm. The main limitations of the algorithm are:  
1. it ignores multiple forms of overlap and is only applicable to the case of mild overlapping;
2. it is easy to make a mistake in the selection of candidate concave points for multiple overlapping droplets, which leads to segmentation errors. In contrast, the method proposed in this paper has the best performance in terms of the segmentation of various types of overlapping droplets. It can accurately define the boundaries of droplets, and can accurately extract the complete contours of multiple overlapping droplets by ellipse fitting in the subsequent steps, which significantly reduces the phenomena of over-segmentation and under-segmentation, and effectively recovers the droplet edges in the overlapping regions. Based on the segmentation method proposed in this paper, we tested the overlapping water droplet segmentation on 105 water-sensitive test paper images collected in the orchard, and compared and analysed the segmentation results with those of manual calibration.