Nitrogen Content Inversion of Corn Leaf Data Based on Deep Neural Network Model

Yulin Li, Mengmeng Zhang*, Maofang Gao*, Xiaoming Xie*, Wei Li

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

To obtain excellent regression results under the condition of small sample hyperspectral data, a deep neural network with simulated annealing (SA-DNN) is proposed. According to the characteristics of data, the attention mechanism was applied to make the network pay more attention to effective features, thereby improving the operating efficiency. By introducing an improved activation function, the data correlation was reduced based on increasing the operation rate, and the problem of over-fitting was alleviated. By introducing simulated annealing, the network chose the optimal learning rate by itself, which avoided falling into the local optimum to the greatest extent. To evaluate the performance of the SA-DNN, the coefficient of determination (R2), root mean square error (RMSE), and other metrics were used to evaluate the model. The results show that the performance of the SA-DNN is significantly better than other traditional methods.

源语言英语
页(从-至)619-630
页数12
期刊Journal of Beijing Institute of Technology (English Edition)
32
5
DOI
出版状态已出版 - 2023

指纹

探究 'Nitrogen Content Inversion of Corn Leaf Data Based on Deep Neural Network Model' 的科研主题。它们共同构成独一无二的指纹。

引用此