Prediction of dust concentration on a laboratory scale using image processing and artificial intelligence technologies

Dust is one of the environmental issues that have adverse effects in all sectors of agriculture and natural resources. The aim of this research is to predict the concentration of dust in the air. A laboratory system for acquiring images of dust was implemented including a glass chamber, a blower, a dust meter, an imaging camera, and a personal computer. Using clay soil, dust storms with different concentrations of 0, 275, 1289, 1896, 2316, 2585, and 2750 µg/m3 were created inside the glass chamber. For each studied dust concentration, 15 images were obtained and after their preprocessing, the mean values of different image channels in various color spaces were extracted. The features of the images were used to predict dust concentration using artificial intelligence technology. The data were divided into three groups, 60% of the data were used for training, 20% for validation, and 20% for testing the network. Different models of multilayer perceptron artificial neural networks were investigated and 10-8-1 structure with tansig activation function in hidden and output layers has the highest accuracy (93.81%). The findings of the present research show the high capability of image processing and artificial intelligence technologies in predicting dust concentration with high accuracy and low cost.