Combining spectral and texture features of UAV hyperspectral images for leaf nitrogen content monitoring in winter wheat

ABSTRACT

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a ‘graph–spectrum’ fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the ‘graph–spectrum’ information formed by eight parameters, including the normalized vegetation index NDVI (R₅₇₈, R₄₉₀), the difference vegetation index DVI (R₈₃₀, R₇₇₈), MEA₄₉₀, MEA₇₇₈, VAR₄₉₀, VAR₅₇₈, VAR₇₇₈, and HOM₅₇₈ as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R²), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring.

KEYWORDS:

• Winter wheat
• Leaf nitrogen content
• Unmanned aerial vehicle hyperspectral imaging data
• Vegetation indexes
• Textural features
• Machine learning

Acknowledgements

The authors would like to thank TopEdit (www.topeditsci.com) for linguistic assistance during preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Supplementary material related to this article available from the corresponding author on reasonable request.

Additional information

Funding

This work was supported by grants from the Key Scientific and Technological Projects of Henan Province (192102110012), the National Key Research and Development Program of China (2016YFD0300609), and Henan Modern Agriculture (Wheat) Research System (S2010-01-G04); Henan Modern Agriculture (Wheat) Research System [S2010-01-G04]