Pieloth, DamianRodeck, MatthiasSchaldach, GerhardThommes, Markus2023-10-202023-10-202022-11-04http://hdl.handle.net/2003/42164http://dx.doi.org/10.17877/DE290R-23997Spray characterization has been an issue for process and product characterization for decades. Because of this, a convolutional neuronal network was developed to determine the droplet size from spray images. The images were taken using a digital camera, a light source, and a dark room. These were subsequently employed to design and train a convolutional neuronal network using open-source software packages and a desktop computer. The accuracy of the network droplet size determinations was checked with additional, independent images. The median drop size was assessed with a high accuracy of more than 99.8 % as the mean spray performance indicator. Additionally, the droplet size distribution measurements from the neural network method deviated from those from the reference method (laser diffraction) by less than 1.5 %. Convolutional neuronal networks can be applied to determine the spray performance using spray cone images. This approach could be useful for multiple applications.enConvolutional neural networksDroplet sizeImage analysisMachine learningSpray categorization660TextZellulares neuronales NetzTropfengrößeBildanalyseMaschinelles LernenZerstäubung