TY - JOUR
T1 - Nitrogen Content Inversion of Corn Leaf Data Based on Deep Neural Network Model
AU - Li, Yulin
AU - Zhang, Mengmeng
AU - Gao, Maofang
AU - Xie, Xiaoming
AU - Li, Wei
N1 - Publisher Copyright:
© 2023 Beijing Institute of Technology. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - deep neural network
KW - nitrogen content detection
KW - precision agriculture
KW - regression model
UR - http://www.scopus.com/inward/record.url?scp=85184991873&partnerID=8YFLogxK
U2 - 10.15918/j.jbit1004-0579.2023.034
DO - 10.15918/j.jbit1004-0579.2023.034
M3 - Article
AN - SCOPUS:85184991873
SN - 1004-0579
VL - 32
SP - 619
EP - 630
JO - Journal of Beijing Institute of Technology (English Edition)
JF - Journal of Beijing Institute of Technology (English Edition)
IS - 5
ER -