Classification Model of Wheat Grain based on Autoencoder

Deep learning technology is the latest hot technology in the fields of machine learning, artificial intelligence, data mining, and pattern recognition in recent years; it is based on the hierarchical structure of the human brain and achieves the purpose of automatic parameter adjustment through training mechanisms, enabling some complex tasks to be performed. Simplified or implemented by deep learning technology. This article comprehensively describes the typical structure of deep learning models, introduces popular deep learning models such as autoencoders, and uses this model to classify and identify diseased wheat in the wheat particle database. Experiments quantitative statistical results show that by adjusting the parameters of deep learning during the training of the autoencoder network, the training error of the training sample will be smaller, and the error of the test sample will be reduced.