Abstract: Monitoring moisture condition of cucumber leaves is to calculate leaf wetness duration for the disease forecasting in greenhouse, which is especially important for improving the yield and quality of agricultural products. K-means clustering with opening and closing alternately filtering algorithm was used for the fluorescence images segmentation of water droplets on cucumber leaves. The healthy and clean cucumber leaves in the artificial climate chamber were chosen as the experimental materials. In the experiment, we used a pipette with different volume of water (100 or 200 mL) to drop water on cucumber leaves. Each time, water was dropped to different parts of the cucumber leaves, including leaf surface and margin to simulate different leaf wetness situation. We used the fluorescence imaging instrument to collect the image at day and night. In this article, the image segmentation method was divided into two parts, which included K- means clustering and opening and closing alternately filtering. The main steps of segmentation algorithm of water droplet fluorescence image were as follows. The original images were collected in RGB color space, but the color distribution of the RGB color space was uneven. The advantages of the L*a*b* color space could make up for the shortage. So the original image was firstly converted to the L*a*b* color space from RGB color space. In the L*a*b* color space, all color information was contained in a* and b* components. Secondly the color difference between the two-dimensional data space of a* and b* was used, and Euclidean distance was chosen to measure the similarity between pixels. The fluorescence images were clustered by K- means. After finish of the clustering operation, the images were grayed, and then they were corrected by use of mathematical morphology methods. For morphology methods operation steps, open operation was firstly applied and then close operation was applied. The operations were repeated until the desired results were obtained. And finally the image segmentation was completed. The experiment was carried out to segment ten fluorescence images of cucumber leaves with different numbers of water droplets. In order to verify the validity of the method, we compared our results with three other segmentation algorithms based on H component, active contour model (C_V model), fusion of K-means and Ncut. The results showed that the average matching rate was 81.27% and the average misclassification rate of this method was 9.57%. Compared with the three methods, the average matching rate from our method was improved by 44.11%, 11.50% and 10.90%, respectively. In comparison to the three methods, the average misclassification rate of the method was reduced by 23.03%, 5.47% and 5.05%, respectively. From the experimental data, the segmentation results of the fluorescence images were satisfactory. This method can be used to segment water droplets from the fluorescence images of water droplets on cucumber leaves accurately, which provides a new way to monitor the wetness duration of cucumber leaves by computer vision.