Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery

Hongyan Yang; Jixuan Yan; Guang Li; Weiwei Ma; Xiangdong Yao; Jie Li; Qihong Da; Kejing Cheng

doi:10.3390/drones9040270

journal article Open Access Apr 01, 2025

Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery

Hongyan Yang

Jixuan Yan Guang Li

Weiwei Ma

Xiangdong Yao

Jie Li

Qihong Da Kejing Cheng

Drones Vol. 9 No. 4 pp. 270 · MDPI AG

View at Publisher Save 10.3390/drones9040270

Abstract

Accurately monitoring total nitrogen (TN) content in field soils is crucial for precise fertilization management. TN content is one of the core indicators in soil fertility evaluation systems. Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. Although UAV-based multispectral remote sensing technology has shown potential in agricultural monitoring, research on its quantitative assessment of soil TN content remains limited. This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. A total of 18 models based on machine learning algorithms, including BP neural networks (BPNNs), random forest (RF), and partial least squares regression (PLSR), were constructed to compare the most suitable inversion model for TN in the rhizosphere soil (0–30 cm) of silage corn at different growth stages. The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. Among the nitrogen inversion models based on different machine learning algorithms, the PLSR model showed the best performance, followed by the RF model, while the BPNN model performed the worst. The PLSR model established for the mature growth stage at soil depths demonstrated the highest inversion accuracy, with R and RMSE values of 0.663 and 0.281, respectively. The next best period was the tasseling stage, while the worst inversion accuracy was observed during the seedling stage, indicating that the mature stage is the optimal period for TN inversion in the study area.

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References

Details

Published: Apr 01, 2025
Vol/Issue: 9(4)
Pages: 270
License: View

Authors

H

Hongyan Yang

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

J

Jixuan Yan

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

G

Guang Li

State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China; College of Forestry, Gansu Agricultural University, Lanzhou 730070, China

W

Weiwei Ma

State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China; College of Forestry, Gansu Agricultural University, Lanzhou 730070, China

X

Xiangdong Yao

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

J

Jie Li

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

Q

Qihong Da

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

K

Kejing Cheng

College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China; State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China

Funding

National Natural Science Foundation of China Award: 24ZD13NA019

Gansu Provincial Science and Technology Major Special Project Award: 24ZD13NA019

Central guide local science and technology development special funds Award: 24ZD13NA019

Gansu Provincial Key Research and Development Project for Ecological Civilization Construction Award: 24ZD13NA019

Gansu Provincial Department of Education Industry Support Plan Project Award: 24ZD13NA019

Cite This Article

Hongyan Yang, Jixuan Yan, Guang Li, et al. (2025). Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery. Drones, 9(4), 270. https://doi.org/10.3390/drones9040270

Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery

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