Objective To solve the problem of low detection accuracy in existing nondestructive detection methods for navel orange sugar content.Methods Based on the analysis of detection schemes, a multi-information integration method for nondestructive detection of navel orange sugar content is proposed. Data collection is conducted using spectral detection technology, machine vision technology, and electronic nose technology. Spectral data is obtained by competitive adaptive weighted sampling of 17 wavelength variables. The principal component analysis is applied to extract 6 features from machine vision data and 4 features from electronic nose sensor data, which are then used as inputs to an improved RBF neural network model for sugar content detection.Results Compared with conventional detection methods, the proposed multi-information integration method extracts features more comprehensively, resulting in higher detection accuracy and efficiency. The coefficient of determination is 0.960 8, the root mean square error is 0.083 2 °Brix, and the average detection time is 0.154 s.Conclusion This scheme improves the detection accuracy of navel orange sugar content and has certain reference value.