Objective:To explore the application ability of machine vision and electronic nose fusion method in fruit and vegetable maturity detection, and realize the detection of different maturity of tomato.Methods:Based on machine vision and electronic nose acquisition system, a tomato maturity detection method based on multi-source information fusion was proposed. Based on 6 color features screened by machine vision and 10 odor features screened by electronic nose, the least squares support vector machine model for tomato maturity detection was established. The feasibility of this method is verified by comparing the fusion method with single method.Results:Compared with the single detection method, the multi-source fusion method improves the accuracy of tomato maturity recognition, tomato hardness and lycopene prediction.Conclusion:The multi-source fusion method improves the detection ability of fruit and vegetable maturity to a certain extent.