Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experiment Location and Research Material
2.2. Data Collection and Processing
2.3. Method
2.3.1. Continuous Wavelet Transform
2.3.2. Spectral Differential Transformation
2.3.3. Construction of Vegetation Indices
2.3.4. Machine Learning Methods
2.3.5. Correlation Analysis
2.3.6. Model Accuracy Evaluation Indicators
3. Results
3.1. Spectral Response Characteristics of Wheat Leaves at Different Positions
3.2. Construction of Nitrogen Content Model Based on the Spectral Reflectance
3.3. Construction of the Nitrogen Content Model Based on Vegetation Indices
3.4. Construction of the Nitrogen Content Model Based on the Wavelet Coefficients
4. Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
FD | First-order Differential |
SD | Second-order Differential |
CR | Continuous Removal |
PLSR | Partial Least Squares Regression |
SVM | Support Vector Machine |
RF | Random Forest |
MLR | Multiple Linear Regression |
CWT | Continuous Wavelet Transform |
R2 | Coefficient of Determination |
RMSE | Root Mean Squared Error |
NRMSE | Normalized Root Mean Squared Error |
NDVI | Normalized Difference Vegetation Index |
RVI | Ratio Vegetation Index |
DVI | Difference Vegetation Index |
OSAVI | Optimized Soil-Adjusted Vegetation Index |
VIOPT | Optimal Vegetation Index |
NDWI | Normalized Difference Water Index |
WI | Water Index |
TCARI | Transformed Chlorophyll Absorption Ratio |
CI | red edge Chlorophyll Index Red Edge |
RGVI | Red-Green Vegetation Index |
RBVI | Red-Blue Vegetation Index |
GBVI | Green-Blue Vegetation Index |
MGRVI | Misra Green-Red Vegetation Index |
RGBVI | Relative Green-Blue Vegetation Index |
GLA | Green Leaf Area |
EXR | Excess Red |
EXG | Excess Green |
EXGR | Excess Green Minus Excess Red |
CIVE | Color Index of Vegetation |
VARI | Visible Atmospherically Resistant Indices |
References
- Tao, H.; Feng, H.; Xu, L.; Miao, M.; Yang, G.; Yang, X.; Fan, L. Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors 2020, 20, 1231. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, M.; Adeel, H.M.; Xu, K.; Zheng, C.; Awais, R.; Zhang, Y.; Jin, X.; Xia, X.; Xiao, Y.; He, Z. Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat. Front. Plant Sci. 2020, 11, 927. [Google Scholar] [CrossRef] [PubMed]
- Su, W.; Wang, W.; Liu, Z.; Zhang, M.; Bian, H.; Cui, Y.; Huang, J. Determining the retrieving parameters of corn canopy LAI and chlorophyll content computed using UAV image. Trans. Chin. Soc. Agric. Eng. 2020, 36, 58–65. [Google Scholar] [CrossRef]
- Han, Q.; Zhang, X.; Wang, S.; Zhang, L.; Zhang, X.; Tian, J. Sensitivity research on the canopy hyperspectral inversion for the typical multi-parameter of winter wheat. Sci. Technol. Eng. 2017, 17, 89–97. [Google Scholar] [CrossRef]
- Huang, S.; Hong, T.; Yue, X.; Wu, W.; Cai, K.; Xu, X. Multiple regression analysis of citrus Leaf nitrogen content using hyperspectral technology. Trans. Chin. Soc. Agric. Eng. 2013, 29, 132–138. [Google Scholar] [CrossRef]
- Yue, X.; Quan, D.; Hong, T.; Liu, Y.; Wu, M.; Duan, J. Estimation model of nitrogen content for citrus leaves by spectral technology based on manifold learning algorithm. Trans. Chin. Soc. Agric. Mach. 2015, 46, 244–250. [Google Scholar] [CrossRef]
- Heidmann, T.; Thomsen, A.; Schelde, K. Modelling soil water dynamics in winter wheat using different estimates of canopy development. Ecol. Model. 2000, 129, 229–243. [Google Scholar] [CrossRef]
- Dang, R.J.; Li, S.Q.; Mu, X.H.; Li, S.X. Effect of nitrogen on nitrogen vertical distribution and chlorophyll relative value of winter wheat canopy in sub-humid areas. J. Northwest Flora 2008, 28, 182–188. [Google Scholar] [CrossRef]
- Luo, X.; Chen, B.; Zhang, J.; Jiang, P.; Lou, S.; Peng, X.; He, J. Study on the spatial distribution of leaf N content and SPAD value in cotton. Cotton Sci. 2009, 21, 427–430. [Google Scholar] [CrossRef]
- Zhai, L.; Wei, F.; Feng, H.; Li, C.; Yang, G.; Wu, Z.; Liu, M.; Miao, M. Analysis of spectral characteristics and vertical distribution of nitrogen in winter wheat under different water treatments. China Agric. Inform. 2019, 31, 39–54. [Google Scholar]
- Capobianco, G.R.; Fernando, P.; André, F.; Colnago, C.R.; Gustavo, C.L.; Suda, N.J. CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms. Int. J. Wavelets Multiresolution Inf. Process. 2020, 18, 2030001. [Google Scholar] [CrossRef]
- Fan, R.; Xing, L.; Pan, J.; Shan, X.; You, J.; Li, C.; Zhong, W. Study of the Relationship Between the Oil Content of Oil Sands and Spectral Reflectance Based on Spectral Derivatives. J. Indian Soc. Remote Sens. 2019, 47, 931–940. [Google Scholar] [CrossRef]
- Tang, Q.; Li, S.; Wang, K.; Xie, R.; Chen, B.; Wang, F.; Diao, W.; Xiao, C. Monitoring canopy nitrogen status in winter wheat of growth anaphase with hyperspectral remote sensing. Guang Pu Xue Yu Guang Pu Fen Xi Guang Pu 2010, 30, 3061–3066. [Google Scholar] [CrossRef] [PubMed]
- Fern, R.R.; Foxley, E.A.; Bruno, A.; Morrison, M.L. Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecol. Indic. 2018, 94, 16–21. [Google Scholar] [CrossRef]
- Feng, L.; Zhang, Z.; Ma, Y.; Du, Q.; Williams, P.; Drewry, J.; Luck, B. Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens. 2020, 12, 2028. [Google Scholar] [CrossRef]
- Schuh, W.-D. The Processing of Band-Limited Measurements; Filtering Techniques in the Least Squares Context and in the Presence of Data Gaps. Space Sci. Rev. 2003, 108, 67–78. [Google Scholar] [CrossRef]
- Shen, H.; Jiang, K.; Sun, W.; Xu, Y.; Ma, X. Irrigation decision method for winter wheat growth period in a supplementary irrigation area based on a support vector machine algorithm. Comput. Electron. Agric. 2021, 182, 106032. [Google Scholar] [CrossRef]
- Hu, Y.M.; Liang, Z.M.; Liu, Y.W.; Wang, J.; Yao, L.; Ning, Y. Uncertainty analysis of SPI calculation and drought assessment based on the application of Bootstrap. Int. J. Climatol. 2015, 35, 1847–1857. [Google Scholar] [CrossRef]
- McCann, C.M.; Baylis, M.; Williams, D.J.L. The development of linear regression models using environmental variables to explain the spatial distribution of Fasciola hepatica infection in dairy herds in England and Wales. Int. J. Parasitol. 2010, 40, 1021–1028. [Google Scholar] [CrossRef]
- Li, C.C.; Shi, J.J.; Ma, C.Y.; Cui, Y.Q.; Wang, Y.L.; Li, Y.C. Estimation of Chlorophyll Content in Winter Wheat Based on using canpoy digital images from cellphone camera. Trans. Chin. Soc. Agric. Mach. 2021, 52, 172–182. [Google Scholar] [CrossRef]
- Cheng, T.; Rivard, B.; Sánchez-Azofeifa, A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ. 2010, 115, 659–670. [Google Scholar] [CrossRef]
- Ding, Y.; Zhang, J.; Sun, H.; Li, X. Sensitive bands extraction and prediction model of tomato chlorophyll in glass greenhouse. Spectrosc. Spectr. Anal. 2017, 37, 194–199. [Google Scholar] [CrossRef]
- Xiao, C.; Li, S.; Wang, K.; Lu, Y.; Bai, J.; Xie, R.; Gao, S.; Li, X.; Tan, H.; Wang, Q. The Response of Canopy Direction Reflectance Spectrum for the Wheat Vertical Leaf Distributing. Sens. Lett. 2011, 9, 1069–1074. [Google Scholar] [CrossRef]
Name | Formula |
---|---|
Normalized Difference Vegetation Index (NDVI) | (Rnir − Rred)/(Rnir + Rred) |
Ratio Vegetation Index (RVI) | Rnir/Rred |
Difference Vegetation Index (DVI) | Rnir-Rred |
Optimized Soil-Adjusted Vegetation Index (OSAVI) | 1.16 × (Rnir − Rred)/(Rnir + Rred + 0.16) |
Optimal Vegetation Index (VIOPT) | 1.45 × (RniR2 + 1) × (Rred + 0.45) |
Normalized Difference Water Index (NDWI) | (R860 − R1240)/(R860 + R1240) |
Water Index (WI) | R900/R970 |
Transformed Chlorophyll Absorption Ratio (TCARI) | 3× ((R700 − R670) − 0.2 × (R700 − R550) × (R700/R670)) |
Chlorophyll Index Red Edge (CI red edge) | (Rnir/Rred edge) − 1 |
Red-Green Vegetation Index (RGVI) | Rred/Rgreen |
Red-Blue Vegetation Index (RBVI) | Rred/Rblue |
Green-Blue Vegetation Index (GBVI) | Rgreen/Rblue |
Misra Green-Red Vegetation Index (MGRVI) | (R2green − R2red)/(R2green + R2red) |
Relative Green-Blue Vegetation Index (RGBVI) | (R2green − Rblue × Rred)/(R2green +Rblue × Rred) |
Green Leaf Area (GLA) | (2 × Rgreen – Rred − Rblue)/(2 × Rgreen + Rred − Rblue) |
Excess Red (EXR) | 1.4 × Rred − Rgreen |
Excess Green (EXG) | 2 × Rgreen – Rred − Rblue |
Excess Green Minus Excess Red (EXGR) | 2 × (Rgreen – Rred − Rblue) − 1.4 × (Rred + green) |
Color Index of Vegetation (CIVE) | 0.441 × Rred − 0.881 × Rgreen + 0.3856 × Rblue + 18.79 |
Visible Atmospherically Resistant Indices (VARI) | (Rgreen − Rred)/(Rgreen + Rred − Rblue) |
Sort | Flag Leaf | Upper 1 Leaf | Upper 2 Leaf | Upper 3 Leaf | Upper 4 Leaf | |||||
---|---|---|---|---|---|---|---|---|---|---|
1 | FD (649) | 0.89 | FD (929) | 0.84 | FD (1232) | 0.76 | FD (727) | 0.75 | FD (1421) | 0.67 |
2 | FD (659) | 0.89 | FD (950) | 0.82 | FD (1234) | 0.76 | FD (621) | 0.74 | FD (1610) | 0.59 |
3 | SD (685) | 0.89 | FD (926) | 0.83 | FD (1229) | 0.75 | SD (543) | 0.74 | FD (1418) | 0.59 |
4 | SD (748) | 0.88 | FD (927) | 0.83 | FD (1233) | 0.74 | SD (589) | 0.73 | FD (1598) | 0.57 |
5 | SD (780) | 0.87 | SD (925) | 0.83 | FD (1231) | 0.74 | SD (542) | 0.73 | FD (1618) | 0.57 |
Leaf Position | Model | Modeling Accuracy | Verification Accuracy | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | ||
Flag leaf | PLSR | 0.61 ** | 0.08 | 0.06 | 0.58 ** | 0.19 | 0.14 |
SVM | 0.47 ** | 0.09 | 0.08 | 0.42 | 0.23 | 0.21 | |
RF | 0.26 ** | 0.12 | 0.09 | 0.23 | 0.22 | 0.16 | |
MLR | 0.55 ** | 0.08 | 0.06 | 0.55 ** | 0.19 | 0.13 | |
Upper 1 leaf | PLSR | 0.47 ** | 0.16 | 0.09 | 0.55 ** | 0.16 | 0.09 |
SVM | 0.37 ** | 0.16 | 0.18 | 0.36 | 0.20 | 0.22 | |
RF | 0.28 ** | 0.18 | 0.11 | 0.30 * | 0.20 | 0.12 | |
MLR | 0.55 ** | 0.14 | 0.08 | 0.57 ** | 0.16 | 0.10 | |
Upper 2 leaf | PLSR | 0.43 ** | 0.09 | 0.08 | 0.52 ** | 0.11 | 0.09 |
SVM | 0.32 ** | 0.10 | 0.20 | 0.35 | 0.11 | 0.21 | |
RF | 0.32 ** | 0.10 | 0.08 | 0.38 * | 0.11 | 0.09 | |
MLR | 0.39 ** | 0.09 | 0.08 | 0.47 ** | 0.11 | 0.10 | |
Upper 3 leaf | PLSR | 0.13 ** | 0.13 | 0.12 | 0.11 | 0.13 | 0.12 |
SVM | 0.42 ** | 0.13 | 0.22 | 0.45 ** | 0.15 | 0.26 | |
RF | 0.12 | 0.10 | 0.10 | 0.21 | 0.16 | 0.14 | |
MLR | 0.15 * | 0.13 | 0.12 | 0.17 | 0.12 | 0.11 | |
Upper 4 leaf | PLSR | 0.17 * | 0.11 | 0.14 | 0.20 | 0.14 | 0.18 |
SVM | 0.40 ** | 0.12 | 0.19 | 0.42 ** | 0.13 | 0.20 | |
RF | 0.11 | 0.11 | 0.14 | 0.15 | 0.15 | 0.18 | |
MLR | 0.17 * | 0.09 | 0.11 | 0.14 | 0.14 | 0.18 |
Sort | Flag Leaf | Upper 1 Leaf | Upper 2 Leaf | Upper 3 Leaf | Upper 4 Leaf | |||||
---|---|---|---|---|---|---|---|---|---|---|
1 | EXR (red green) | 0.78 | GBVI (green blue) | 0.75 | WI (900 970) | 0.70 | NDWI (860 1240) | 0.66 | TCARI (700 670 550) | 0.48 |
2 | GLA (red green blue) | 0.78 | MGRVI (green red) | 0.74 | NDWI (860 1240) | 0.58 | WI (900 970) | 0.64 | GIVE (red green blue) | 0.47 |
3 | RGBVI (red green blue) | 0.77 | RGVI (red green) | 0.74 | NDVI (nir red) | 0.39 | VIOPT (nir red) | 0.35 | VIOPT (nir red) | 0.44 |
4 | GBVI (green red) | 0.77 | GLA (green blue red) | 0.74 | OSAVI (nir red) | 0.38 | DVI (nir red) | 0.36 | WI (900 970) | 0.37 |
5 | MGRVI (green red) | 0.77 | EXR (red green) | 0.74 | RVI (nir red) | 0.39 | TCARI (670 700) | 0.33 | Red Edge | 0.36 |
Leaf Position | Model | Modeling Accuracy | Verification Accuracy | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | ||
Flag leaf | PLSR | 0.68 ** | 0.07 | 0.06 | 0.58 ** | 0.10 | 0.09 |
SVM | 0.51 ** | 0.08 | 0.16 | 0.58 ** | 0.12 | 0.22 | |
RF | 0.49 ** | 009 | 0.07 | 0.45 ** | 0.12 | 0.10 | |
MLR | 0.64 ** | 0.08 | 0.06 | 0.54 ** | 0.09 | 0.08 | |
Upper 1 leaf | PLSR | 0.60 ** | 0.14 | 0.08 | 0.57 ** | 0.18 | 0.11 |
SVM | 0.59 ** | 0.13 | 0.15 | 0.52 ** | 0.17 | 0.19 | |
RF | 0.45 ** | 0.15 | 0.09 | 0.43 ** | 0.19 | 0.11 | |
MLR | 0.55 ** | 0.13 | 0.08 | 0.51 ** | 0.17 | 0.10 | |
Upper 2 leaf | PLSR | 0.33 ** | 0.16 | 0.12 | 0.28 * | 0.12 | 0.09 |
SVM | 0.53 ** | 0.09 | 0.08 | 0.49 ** | 0.21 | 0.19 | |
RF | 0.38 ** | 0.10 | 0.07 | 0.48 ** | 0.23 | 0.17 | |
MLR | 0.54 ** | 0.14 | 0.10 | 0.53 ** | 0.11 | 0.09 | |
Upper 3 leaf | PLSR | 0.44 ** | 0.10 | 0.10 | 0.52 ** | 0.10 | 0.10 |
SVM | 0.42 ** | 0.11 | 0.16 | 0.32 * | 0.13 | 0.22 | |
RF | 0.24 ** | 0.10 | 0.10 | 0.44 ** | 0.15 | 0.14 | |
MLR | 0.50 ** | 0.09 | 0.08 | 0.52 ** | 0.11 | 0.11 | |
Upper 4 leaf | PLSR | 0.31 ** | 0.08 | 0.10 | 0.28 * | 0.13 | 0.17 |
SVM | 0.33 ** | 0.10 | 0.16 | 0.31 * | 0.11 | 0.16 | |
RF | 0.16 * | 0.10 | 0.12 | 0.12 | 0.13 | 0.16 | |
MLR | 0.37 ** | 0.08 | 0.10 | 0.35 * | 0.13 | 0.16 |
Sort | Flag Leaf | Upper 1 Leaf | Upper 2 Leaf | Upper 3 Leaf | Upper 4 Leaf | |||||
---|---|---|---|---|---|---|---|---|---|---|
1 | C4 (2173) | 0.87 | C3 (1701) | 0.87 | C3 (1695) | 0.79 | C8 (1367) | 0.76 | C5 (1745) | 0.63 |
2 | C4 (2172) | 0.87 | C3 (1703) | 0.87 | C3 (1694) | 0.79 | C8 (1366) | 0.76 | C5 (1746) | 0.63 |
3 | C4 (2174) | 0.87 | C3 (1700) | 0.87 | C3 (1696) | 0.78 | C8 (1370) | 0.76 | C5 (1744) | 0.63 |
4 | C4 (2171) | 0.87 | C3 (1704) | 0.87 | C3 (1693) | 0.78 | C8 (1369) | 0.76 | C5 (1747) | 0.63 |
5 | C4 (2175) | 0.87 | C3 (2168) | 0.87 | C3 (1697) | 0.78 | C8 (1368) | 0.76 | C5 (1743) | 0.63 |
Leaf Position | Model | Modeling Accuracy | Verification Accuracy | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | ||
Flag leaf | PLSR | 0.79 ** | 0.06 | 0.05 | 0.71 ** | 0.08 | 0.07 |
SVM | 0.66 ** | 0.07 | 0.14 | 0.65 ** | 0.05 | 0.10 | |
RF | 0.74 ** | 0.06 | 0.04 | 0.65 ** | 0.09 | 0.08 | |
MLR | 0.84 ** | 0.06 | 0.05 | 0.89 ** | 0.07 | 0.06 | |
Upper 1 leaf | PLSR | 0.76 ** | 0.10 | 0.06 | 0.79 ** | 0.11 | 0.06 |
SVM | 0.74 ** | 0.08 | 0.09 | 0.78 | 0.12 | 0.13 | |
RF | 0.72 ** | 0.13 | 0.08 | 0.64 ** | 0.09 | 0.05 | |
MLR | 0.77 ** | 0.12 | 0.07 | 0.80 ** | 0.08 | 0.05 | |
Upper 2 leaf | PLSR | 0.60 ** | 0.13 | 0.10 | 0.78 ** | 0.08 | 0.06 |
SVM | 0.67 ** | 0.07 | 0.06 | 0.42 ** | 0.19 | 0.17 | |
RF | 0.64 ** | 0.07 | 0.05 | 0.59 * | 0.18 | 0.13 | |
MLR | 0.73 ** | 0.12 | 0.09 | 0.74 ** | 0.09 | 0.07 | |
Upper 3 leaf | PLSR | 0.53 ** | 0.10 | 0.09 | 0.70 ** | 0.07 | 0.07 |
SVM | 0.52 ** | 0.09 | 0.15 | 0.50 ** | 0.08 | 0.14 | |
RF | 0.54 ** | 0.09 | 0.08 | 0.47 ** | 0.11 | 0.10 | |
MLR | 0.58 ** | 0.09 | 0.08 | 0.73 ** | 0.09 | 0.09 | |
Upper 4 leaf | PLSR | 0.33 ** | 0.10 | 0.13 | 0.55 ** | 0.07 | 0.09 |
SVM | 0.29 ** | 0.10 | 0.16 | 0.42 ** | 0.08 | 0.12 | |
RF | 0.47 ** | 0.09 | 0.11 | 0.35 * | 0.14 | 0.17 | |
MLR | 0.51 ** | 0.06 | 0.08 | 0.57 ** | 0.07 | 0.09 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ma, C.; Zhai, L.; Li, C.; Wang, Y. Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models. Appl. Sci. 2022, 12, 7427. https://doi.org/10.3390/app12157427
Ma C, Zhai L, Li C, Wang Y. Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models. Applied Sciences. 2022; 12(15):7427. https://doi.org/10.3390/app12157427
Chicago/Turabian StyleMa, Chunyan, Liting Zhai, Changchun Li, and Yilin Wang. 2022. "Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models" Applied Sciences 12, no. 15: 7427. https://doi.org/10.3390/app12157427
APA StyleMa, C., Zhai, L., Li, C., & Wang, Y. (2022). Hyperspectral Estimation of Nitrogen Content in Different Leaf Positions of Wheat Using Machine Learning Models. Applied Sciences, 12(15), 7427. https://doi.org/10.3390/app12157427