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Dust is an environmental issue that adversely affects all sectors of agriculture and natural resources. This research aims 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. Dust storms with different concentrations of 0, 275, 1289, 1896, 2316, 2585, and 2750 µg/m3 were created inside the glass chamber using clay soil. For each studied dust concentration, 15 images were obtained, and after their preprocessing, the mean values of different image channels in various colour 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 the 10-8-1 structure with tension activation function in hidden and output layers has the highest accuracy (93.81%). The present research findings show the high capability of image processing and artificial intelligence technologies in predicting dust concentration with high accuracy and low cost. [ABSTRACT FROM AUTHOR]

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