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Magnetic resonance imaging (MRI) technology and artificial neural network theory were used to discriminate the browning disease inside the fruit. Areas corresponding to the core of fragrant pear in T2-weighted image were selected to the region of interest (ROI). Quantitative analysis of the ROI was achieved by extracting ten texture features that reflected the browning characteristics. Back propagation (BP) neural network was carried out on the statistical features to predict the internal browning of fragrant pear. Genetic algorithm (GA) was adopted to optimize the initial weights and threshold in BP neural network. For four groups of samples, the optimization model showed 92.50% accuracy in detecting the presence of browning in fragrant pear, compared with the correct recognition rate 80.83% of the non-optimization, an 11.67 percent increased. For the same group samples, the recognition results of optimized model were also better than the non-optimized model and the correct recognition rate of each group was improved to varying degrees. The result of our experiment shows that the optimized model has good predictive accuracy and generalization ability to identify the internal browning of fragrant pear. 