A Processing Method for Adhesive Droplets on Images of Water-sensitive Papers

Droplet deposition characteristics are essential for evaluating the application of plant protection products concerning the environmental impacts of spraying operations. Over the last decades, water-sensitive papers have been widely used to assess droplet deposition, since they are simple to use and represent a suitable tool for spraying application by farmers. At present, although many droplet analysis software and algorithms already exist, the problem of adhesive droplets is still a thorny issue, which significantly impairs the accuracy of droplet analysis. In this study, an image-processing code was developed by Python and OpenCV to overcome the adhesive droplets problem. The Canny algorithm was adopted to detect the edges of droplets and to segment the droplets from the background. The connecting points of adhesive droplets were determined by locating the concave points on the contours. After segmenting the contours by using the connecting points, an ellipse fitting algorithm based on the least square method was applied to rebuild the single droplets within the adhesive droplets. A trial was conducted to verify the validity of the image-processing code. Results indicated that the proposed code was able to reduce and rebuild the adhesive droplets.