Abstract: Droplets deposition characteristics estimation is helpful to know the pesticides deposition distribution on crops, which is related to the crop spray quality directly, and especially the fast detection method will provide the basis for improving the pesticides spraying technology. Droplets image processing based on the water sensitive paper is one of the most common methods to detect the droplets deposition characteristics. The droplets coverage percentage and coverage density were selected to evaluate deposition distribution in this paper. With the development of science and technology, a smart phone was selected as image acquiring tool to replace the special image acquisition system, and the improved image processing algorithm of the water sensitive paper was developed. There are 3 steps in the image processing algorithm to obtain each droplet stain, which are image preprocessing, droplets area segmentation, and overlapped droplets segmentation. Firstly, the image was enhanced and calibrated by a calibration board, and then the image of water sensitive paper was segmented from the R channel and B channel image and transferred to a gray scale image. Secondly, the blue droplets stain area was segmented from yellow paper background, and the dynamic threshold method based on the pixel position was used to solve the problem of the influence of uneven brightness in this step, in which the gray mean value of an area of 25×25 pixels was calculated as the segmentation threshold for the middle pixel, and the droplets coverage percentage was calculated by the stains pixel number divided by the pixel number of water sensitive paper area. Thirdly, the circulatory segmentation method based on region circularity was designed to segment the multiple-droplet overlapped regions. Based on the statistical analysis, 0.6 was selected as the circularity threshold, less than 0.6 was considered to be overlapped droplets, and first erosion and then dilation based on a coefficient of corrosion and expansion r was performed; next the segmented area circularity was recalculated, the area with the circularity of less than 0.6 was selected again and segmented by the coefficient (r+1), and the erosion-dilation operation would be repeated over and over until the circularity of all stains was greater than 0.6. Finally, the identified droplets were marked as circle, and the droplets coverage density was calculated by droplets number divided by the water sensitive paper area. Experiments were conducted to test and verify the detection advancement of the proposed image processing algorithm. Experimental results showed that the dynamic threshold segmentation method is not affected by the uneven brightness and can extract 92.64% of droplets, and the droplet coverage percentage detection result is 12.57% and 8.74% greater than constant threshold and partitioned threshold method respectively. Moreover, the proposed overlapped droplets segmentation algorithm can segment successfully more than 2 droplets and the long and thin droplets, the accuracy of droplets identification is 97.2%, and the coverage density detection results showed that the relative error between the algorithm in this paper and manual counting is only 3.31%. The results indicated that the proposed image detection algorithm of droplets deposition characteristics is efficient and convenient, and can completely fulfill the demand of droplets deposition characteristics detection in the field, and the corresponding smartphone applications are in development.