Grain Surface Simulator to Averiguate the Overlapping and Noise Problems on Computer Vision Granullometry of Fertilizers

Abstract

The production of food for all the population in the world became the biggest concern. The population continues to grow and the number of farmable lands has been decreasing. To make the lands more productive, fertilizers are used on a larger scale. To guarantee the quality of the product, particle size analysis are made by mechanical sieving. With the time, the wear-out of the sieving in the fertilizer industry the results of the particle size analysis will be erroneous. So the computer vision appears as an alternative that is non-invasive and less time-consuming. In this context, this paper has the objective to develop a grain surface simulator capable of generating virtual images with overlapping grains, since there is a difficulty to obtain annotated data of images of fertilizers. In order to validate the proposed simulator using a DIP algorithm, noises are added in the virtual images to compare with the reality in the industry, to show how well the particle size analysis with computer vision were handled towards adversities. The results of the overlapping analysis show that when the virtual image has a fewer number of grains, the DIP algorithm can identify the majority of grains, consequently with less error in the particle size analysis. Different noises, at different intensities, have their effects analyzed on the algorithm. As the analyzes in this study match with the reality showing the consequences, tendencies, and errors of the overlapping of grains and noises in the images, the simulator developed here matches with reality and is extremely useful to facilitate the study of complex cases of application of visual computing and digital image processing in particle size analysis of fertilizers.