Abstract： To improve the efficiency of agricultural production by artificial intelligence or machine learning, an autoencoder was constructed based on deep neural network to solve the problems of feature extraction and category identification in agricultural data analysis. The proposed network could not only analyze the features of inherent properties in agricultural data, but also could learn the potential advanced features. Based on the Gaussian kernel fuzzy clustering algorithm (AEKFC), the new autoencoder was realized by adding a Gaussian kernel clustering module into the autoencoder, and a new loss function was used to inversely adjust the whole network training to obtain the clustering results gradually. The proposed clustering method was an endtoend deep neural network learning method based on autoencoder. A great number of experiments were conducted on the dataset of agricultural wheat seeds.The results show that compared with other clustering algorithms,the proposed clustering algorithm has better clustering performance. The new machine learning algorithm can improve the effect of agricultural data analysis and has extensive application value.
