## 5 Conclusions  
In this paper, we propose a multi-level overlapping-droplet segmentation method based on the optimised XGBoost classification model and combined with improved concave-point matching, which adopts different segmentation methods for different types of overlapping droplets by identifying the overlapping droplets after segmentation, separating the complex overlapping droplets step-by-step until we obtain the single droplet or to a state that cannot be continued to be segmented, and, finally, obtaining the final single droplet contour of the overlapping region through ellipse fitting. The final single droplet contour is obtained by ellipse fitting to achieve cyclic iterative segmentation. Through this multi-level iterative optimisation method, accurate and effective segmentation of overlapping droplets with complex shapes is achieved, which improves the segmentation accuracy and efficiency and can be adapted to various complex overlapping situations. Experimental results show that the overall segmentation accuracy of this method is 91.35%, which is significantly better than that of the contour solidity and watershed-based algorithm (76.19%) and the existing improved-concave-point-matching algorithm (68.82%). The average time consumed for segmenting overlapping water droplets on each WSP is within 2.66 s, which is a reasonably high processing speed. Compared with several existing state-of-the-art methods, this method has significant advantages, which not only effectively solves the problem of the low segmentation accuracy of the contour solidity and watershed-based algorithm when dealing with overlapping or large overlapping droplets, but also greatly improves the non-segmentation phenomenon of the existing optimised concave matching algorithms when segmenting strongly overlapping droplets with a single concave point. The proposed overlapping-droplet segmentation method shows great potential for application in the field of agricultural pre-cut image detection and analysis, and can significantly improve the extraction accuracy and efficiency of overlapping droplets with different overlapping shapes. In the subsequent study, we can set the segmentation line to be closer to the actual target contour by considering the change in image gradient in the overlapping region. Secondly, the ellipse fitting error of overlapping droplets is calculated to judge the concave-point attribution so as to accurately extract the contours of individual droplets in the overlapping region. Finally, the algorithm can be considered to be further optimised in complex scenarios (e.g., dynamic lighting and strong wind disturbance) by combining with deep learning techniques [34] to improve its robustness and adaptability. The method is not only valuable in the field of agriculture, but can also be widely used in other fields, such as medical and industrial. In the medical field, the method can be used for biological-tissue segmentation to help identify and separate regions of cells or tissues that overlap with each other, which is crucial for analysing the boundaries between tumours and normal tissues. In the manufacturing industry, the proposed technique can be used to detect and identify defective areas on product surfaces caused by dirt, grease, etc. By accurately identifying these areas, the accuracy of quality control can be greatly improved. Conceptualization, D.L. and X.C. ; data curation, D.L., X.C., P.F. and M.L. ; formal analysis, X.C., M.L., Y.Z., P.F., J.L. and X.W. ; funding acquisition, P.F. and X.C. ; methodology, D.L., X.C. and P.F. ; project administration, P.F. and X.C. ; supervision, P.F. and M.L. ; visualization, D.L. and P.F. ; writing--original draft, D.L., X.C., M.L., Z.L., P.F., J.L. and X.W. ; and writing--review and editing, X.C., D.L. and P.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (grant number 2022 YFD1600600). Not applicable.