Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery
In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of s...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/5/1240 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850254833922080768 |
|---|---|
| author | Yakun Zhang Mengxin Guan Libo Wang Xiahua Cui Yafei Wang Peng Li Shaukat Ali Fu Zhang |
| author_facet | Yakun Zhang Mengxin Guan Libo Wang Xiahua Cui Yafei Wang Peng Li Shaukat Ali Fu Zhang |
| author_sort | Yakun Zhang |
| collection | DOAJ |
| description | In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of soybean canopy at different growth periods under different fertilization treatments were acquired. Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. On this basis, the established prediction models for soybean canopy nitrogen content were compared and analyzed, and the optimal prediction model for soybean canopy nitrogen content was determined. To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R<sub>552</sub>,R<sub>555</sub>), RSI(R<sub>537</sub>,R<sub>573</sub>), and DSI(R<sub>540</sub>,R<sub>555</sub>), was optimal, with R<sup>2</sup> of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R<sup>2</sup> between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth. |
| format | Article |
| id | doaj-art-aa822a9b2a3e4ac29f7315f3d630b7e7 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-aa822a9b2a3e4ac29f7315f3d630b7e72025-08-20T01:57:01ZengMDPI AGAgronomy2073-43952025-05-01155124010.3390/agronomy15051240Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral ImageryYakun Zhang0Mengxin Guan1Libo Wang2Xiahua Cui3Yafei Wang4Peng Li5Shaukat Ali6Fu Zhang7College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Food Bioengineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130025, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaWah Engineering College, University of Wah, Quaid Avenue, Wah Cantt, District Rawalpindi, Punjab 47040, PakistanCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaIn order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of soybean canopy at different growth periods under different fertilization treatments were acquired. Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. On this basis, the established prediction models for soybean canopy nitrogen content were compared and analyzed, and the optimal prediction model for soybean canopy nitrogen content was determined. To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R<sub>552</sub>,R<sub>555</sub>), RSI(R<sub>537</sub>,R<sub>573</sub>), and DSI(R<sub>540</sub>,R<sub>555</sub>), was optimal, with R<sup>2</sup> of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R<sup>2</sup> between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth.https://www.mdpi.com/2073-4395/15/5/1240soybean canopynitrogen contentUAVcorrelation coefficient analysisspectral index |
| spellingShingle | Yakun Zhang Mengxin Guan Libo Wang Xiahua Cui Yafei Wang Peng Li Shaukat Ali Fu Zhang Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery Agronomy soybean canopy nitrogen content UAV correlation coefficient analysis spectral index |
| title | Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery |
| title_full | Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery |
| title_fullStr | Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery |
| title_full_unstemmed | Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery |
| title_short | Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery |
| title_sort | research on monitoring nitrogen content of soybean based on hyperspectral imagery |
| topic | soybean canopy nitrogen content UAV correlation coefficient analysis spectral index |
| url | https://www.mdpi.com/2073-4395/15/5/1240 |
| work_keys_str_mv | AT yakunzhang researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT mengxinguan researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT libowang researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT xiahuacui researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT yafeiwang researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT pengli researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT shaukatali researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery AT fuzhang researchonmonitoringnitrogencontentofsoybeanbasedonhyperspectralimagery |