Abstract: Aiming at the problem that the settlement process in agricultural trade markets and fruit and vegetable supermarkets was not intelligent enough and the difficulty of deploying heavy neural network models, the lightweight recognition method of fruit and vegetable classification model was studied. Firstly, in response to the large differences in the environment where the intelligent recognition equipment for fruits and vegetables was located, and the problem of fuzzy features in fruit and vegetable bagging, a multi-scene collection scheme was used to collect 170 kinds of fruits and vegetables and 136 000 pictures in the fruit and vegetable supermarket, and an image preprocessing scheme for weakened bagging was formulated to further enhance the data. Secondly, aiming at the difficulty of deploying the heavyweight neural network and the high cost, a fruit and vegetable classification recognition model based on Depthwise separable convolution was designed, trained and tested. Its Top-1 success rate had reached 96.8%, and the Top-5 success rate had reached 100%. Compared to Mobilenetv2-224, the amount of computation had been reduced by 70%, compared to Mobilenetv3-224, the amount of computing had also been reduced by 60%, and the recognition ability was higher than Mobilenetv2-224 and lower than Mobilenetv3-224. Finally, the problems faced by the designed fruit and vegetable classification model in the actual deployment were analyzed.

Key words: fruit and vegetable classification, image enhancement, depthwise separable convolution, lightweight neural network