Image analysis system for unmanned aerial spraying system performance evaluation

The use of unmanned aerial spraying systems is increasing due to their many advantages, but there is a lack of research on how to evaluate their performance. In general, the spraying performance is evaluated by collecting the spray droplets with water-sensitive paper and analyzing the images. However, there is a disadvantage that the performance is affected by humidity. In this study, an image analysis program was developed to measure the spraying performance when using pigments and collectors instead of water-sensitive paper. The program was developed in Python and utilizes OpenCV related functions. To overcome the problem of binarization processing, HSV color system was used. The program is able to generate ROIs regardless of the size or shape of the collector and calculate the percentage of deposited area and droplet size distribution of the sprayed droplets.