Study on Soil Total Nitrogen Content Prediction Method Based on Synthetic Neural Network Model

He Liu, Jiamu Wang, Shuyan Liu, Qingran Hu and Dongyan Huang ()
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He Liu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jiamu Wang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Shuyan Liu: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Qingran Hu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Dongyan Huang: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China

Sustainability, 2024, vol. 16, issue 8, 1-20

Abstract: Rational utilization of soil total nitrogen is one of the keys to achieving sustainable agricultural development. By accurately measuring the content of total nitrogen in the soil, the utilization efficiency of nitrogen in the soil can be improved, and the scientific use of chemical fertilizers can reduce the pressure of agriculture on natural resources and realize the sustainable development of agriculture. In order to measure soil total nitrogen content simply and accurately, combined with the method of artificial olfactory systems, a new method of soil total nitrogen content detection based on convolutional noise reduction autoencoder (CDAE)–whale optimization algorithm (WOA)–deep residual shrinkage network (DSRN) is proposed. In order to obtain more salient features for fusion, the channel mechanism of the DSRN is improved by adding global Max pooling. The model uses a CDAE for the first filtering stage to automatically obtain data that filters simple noise and uses the WOA to automatically optimize hyperparameters. Finally, the optimized hyperparameters were used to train the DRSN for secondary filtering and predict the soil total nitrogen content. Experimental results show that the R 2 of CAE-WOA-DSRN test set is 0.968, which is significantly better than the R 2 of a traditional algorithm (0.873) and a simple BP network (0.877), and it can more accurately measure soil total nitrogen content.

Keywords: soil total nitrogen; artificial olfactory system; prediction method; the depth of the residual shrinkage network; convolution encoder (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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