Organic tomatoes identification based on deep learning framework
In order to satisfy the growing demand for organic tomatoes consumption and identification in the consumer market, a deep learning model for rapid identification of organic tomatoes was researched based on mass spectrometry detection data. Firstly, the model used the unsupervised dimensionality reduction method to reduce the dimensionality of the original mass spectrometry detection data and extract key information. Secondly, long short-term memory (LSTM) and Transformer network were used to extract sequence information features. Finally, back propagation (BP) neural network was used to construct classifiers to achieve accurate recognition of organic and non-organic tomatoes. The recognition accuracy of the model was 98.437％ on the training set and 97.478％ on the test set. The results indicated that the model had potential for application in the rapid detection and identification of organic and non-organic tomatoes mass spectrometry tasks, which could partly meet the development needs of the organic tomatoes market and provide references for the identification of organic tomatoes.
deep learningorganic tomatoesdimensionality reductionneural networkslong short-term memory
NETL
NSTL
万方数据
