Traditional fruit and vegetable classification is mostly based on manual operation, which is inefficient. Deep convolution neural network shows excellent performance in feature learning and expression. In this paper, an automatic recognition system of fruits and vegetables based on deep convolution neural network is designed. By using depthwise separable convolution instead of the traditional standard convolution, a neural network is constructed with less parameters, which is suitable for equipment with limited resources. A small data set including 12 kinds of common fruits and 8 kinds of common vegetables is established for training and testing through network download and physical shooting. The experimental results show that the recognition accuracy reaches 95.67%.

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