In order to realize the fast and accurate appearance quality classification of picked fruits,and cooperate with the sorting production line to complete the large-scale centralized sorting of fruits,a fruit classification method based on improved ResNet is proposed in this study.Firstly,the residual module in ResNet network is combined with the dual channel squeeze-and-excitation block (DC-SE Block) to enhance the effective channel features,suppress the inefficient or invalid channel features,and improve the expression ability of the feature map,so as to improve the recognition accuracy.Secondly,the Inception module is added to the original ResNet model to fuse the characteristics of different scales of fruit,so as to enhance the recognition ability of small defects.Finally,four kinds of fruit images with different appearance quality are enhanced,and the model is initialized by transfer learning method.Taking apple as an example,the experimental results show that the accuracy of the improved model trained by the data set is 99.7%,which is higher than 98.5% of the original model;The precision rate is 99.7%,which is higher than 98.3% of the original model;The recall rate reaches 99.7%,which is higher than 98.7% of the original model; The average detection speed under graphic processing unit (GPU) is 32.3 frame/s,which is slightly lower than 35.7 frame/s of the original model.Compared with several advanced classification methods such as GoogleNet and MobileNet,and compared with different improved models,the results show that the proposed method has good classification performance,and has important reference value for solving the problem of accurate classification of fruit appearance quality.