Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inve...

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Bibliographic Details
Published in:Sustainability 2022-10, Vol.14 (19), p.11998
Main Authors: Liu, Zunfang, Lei, Haochuan, Lei, Lei, Sheng, Haiyan
Format: Article
Language:English
Subjects:
Accuracy
Agricultural development
Agricultural production
Back propagation networks
Coefficient of variation
Correlation analysis
Crop yield
Fertilization
Global positioning systems
GPS
Inversion
Laboratories
Least squares method
Machine learning
Methods
Neural networks
Nitrogen
Nitrogen content
Nutrients
Regression analysis
Remote sensing
Soil chemistry
Soil fertility
Soil layers
Soils
Spatial distribution
Support vector machines
Surface layers
Sustainability
Sustainable agriculture
Sustainable development
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Summary:In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R2 = 0.560, RMSE = 0.305) and the PLSR model (R2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development.
ISSN:2071-1050
2071-1050
DOI:10.3390/su141911998