Research on fruit and vegetable classification and recognition method based on Depth-wise Separable Convolution

Abstract

Abstract Aiming at the problem that the settlement process in agricultural trade markets and fruit and vegetable supermarkets is not intelligent enough and the difficulty of deploying heavy neural network models, the lightweight recognition method of fruit and vegetable classification model is studied. Firstly, aiming at the complex environmental problems of fruit and vegetable supermarkets and farmers' markets and the subsequent practical use, 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. Then, a fruit and vegetable classification recognition model based on Depth-wise separable convolution is designed, trained and tested. Its top-1 success rate has reached 96.8%, the top-5 success rate has reached 100%. Compared to Mobilenetv2-224, the amount of computation has been reduced by 70%, compared to Mobilenetv3-224, the amount of computing has 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 are analyzed.