Abstract: In order to discriminate the maturity of upper flue-cured tobacco leaves objectively and accurately and to reduce the error rate caused by subjective factors, the images of upper leaves with different field maturity were collected before harvesting, and the values of color characteristics and texture characteristics of flue-cured tobacco were extracted by MATLAB2018b. A BP neural network model was established to discriminate the harvest maturity of flue-cured tobacco. The results showed that the color characteristic values and texture characteristic values of upper flue-cured tobacco leaves of different maturity levels differed greatly. The established BP neural network model could accurately identify the upper leaves of different maturity levels. The coefficient of determinations of the predicted and actual values of the training samples both reached 0.985 5, and those of the verification samples reached 0.981 9. The established model based on color and texture characteristics was effective for discriminating the maturity of upper flue-cured tobacco leaves.