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6 May 2024 Predictive modeling of nitrogen content in winter wheat plants based on LASSO feature screening and UAV imagery
Yan Guo, Jia He, Huifang Zhang, Kai Zeng, Laigang Wang, Zili Chen, Yan Zhang
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Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131071G (2024) https://doi.org/10.1117/12.3029189
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China
Abstract
Nitrogen is an important nutrient for the yield formation of winter wheat, and the rich spectral and texture information of UAV ultra-high resolution imagery provides an important technical approach for nitrogen accurate prediction. In this study, based on the spectral and texture features extracted from UAV remote sensing images of winter wheat during the key growth stages (jointing stage, booting stage, flowering stage, and filling stage), the LASSO method was introduced to screen feature variables to eliminate the collinearity among the feature variables, and ridge regression, least-squares regression, and LASSO regression were used to construct the nitrogen prediction model in winter wheat plants. When the regularization parameter λ took the value of 0.08, 17 sensitive feature variables such as Nir, RERDVI, NGBDI, con_G, ent_R, mean_R, and mean_Nir were screened out. Based on the screened sensitive characteristic variables, the nitrogen prediction models established by the three methods of ridge regression, least squares regression, and LASSO regression all achieved significant differences at the 0.05 level. The accuracy of the three nitrogen prediction models was highly consistent with R2 of 0.76, 0.77, and 0.78, respectively, and the RMSEs of 3.55g/m2, 3.79g/m2, and 3.79g/m2, respectively. This indicates that the LASSO feature screening method introduced in this study not only makes the model concise but also the model constructed by the sensitive variables screened by LASSO is robust and provides technical support for precise monitoring and management of nitrogen in smart agriculture.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yan Guo, Jia He, Huifang Zhang, Kai Zeng, Laigang Wang, Zili Chen, and Yan Zhang "Predictive modeling of nitrogen content in winter wheat plants based on LASSO feature screening and UAV imagery", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131071G (6 May 2024); https://doi.org/10.1117/12.3029189
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KEYWORDS
Nitrogen
Vegetation
Unmanned aerial vehicles
Agriculture
Atmospheric modeling
Feature extraction
Remote sensing
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