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

作     者：Yulin Li Mengmeng Zhang Maofang Gao Xiaoming Xie Wei Li Yulin Li;Mengmeng Zhang;Maofang Gao;Xiaoming Xie;Wei Li

作者机构：College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijing 100029China School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesChina 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2023年第32卷第5期

页      面：619-630页

核心收录：

学科分类：082804[工学-农业电气化与自动化] 08[工学] 0828[工学-农业工程] 09[农学] 0903[农学-农业资源与环境] 0901[农学-作物学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.62001023,61922013) Beijing Natural Science Foundation(No.4232013) 

主　　题：precision agriculture deep neural network nitrogen content detection regression model 

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 *** to the characteristics of data,the attention mechanism was applied to make the network pay more attention to effective features,thereby improving the operating *** introducing an improved activation function,the data correlation was reduced based on increasing the operation rate,and the problem of over-fitting was *** introducing simulated annealing,the network chose the optimal learning rate by itself,which avoided falling into the local optimum to the greatest *** evaluate the performance of the SA-DNN,the coefficient of determination(R^(2)),root mean square error(RMSE),and other metrics were used to evaluate the *** results show that the performance of the SA-DNN is significantly better than other traditional methods.