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The non-contact evaluation of pesticide inline mixing uniformity based on image processing can promote the development and performance evaluation of the mixers in direct nozzle injection spraying systems (DNIS). In view of the phenomenon that the uniformity results obtained by image processing cannot be directly matched to the traditionally widely accepted and referenced numerical simulation results, the linear models to map the image processing results with numerical simulation results was constructed as inline injecting and mixing viscous water-soluble pesticides and water in a long transparent detection tube, and tested by a jet mixer in a DNIS. Results showed that differing image methods (HSM, OAU, PCA) corresponded to varying optimal linear fitting orders. The optimal order was 4 and the fit goodness was higher than 0.95 when each single image method of them was applied. When each combination of two image methods of them and all the three methods were applied, the order for them can be reduced to 3 and 2, respectively, and the goodness of fit can increase to about 0.98. Based on the above image processing methods and linear models, the uniformity performance of the mixer can be predicted with the error (Mean absolute error, MAE) universally less than 0.05 as the carrier flow rates (Q) were in the range of 800~2000mL/min and the mixing ratios (P) were in the range of 0.01~0.10. Also, the use of univariate and bivariate linear models reduced the average prediction error by 84.1% and 79.8%, respectively, and reduced the variations in prediction results between different algorithms by 31.6% and 78.0%, respectively. The MAE can be limited within 0.03 when univariate models based on the PCA algorithm or the OAU algorithm were applied alone for prediction, and their accuracy was higher than the prediction results of the combinations of different algorithms, indicating the rationality of uniformity prediction using the linear models based on image processing. Though the MAE for the bivariate model based on HSM-PCA algorithm combination was only slightly higher than 0.03, it may have the advantages of avoiding inaccuracy risks of prediction caused by using a single indicator. The research established the relationship between image processing and numerical simulation, thus further improving the feasibility of inline pesticide uniformity assessment inside pipelines based on experiments.