| Estimation of potato leaf water content based on unmanned aerial vehicle hyperspectral remote sensing |
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| DOI：10.7606/j.issn.1000-7601.2026.03.24 |
| Key Words: potato leaf moisture content UAV hyperspectral continuous wavelet transform |
| Author Name | Affiliation | | LIU Kenan | Mechanical and Electrical Engineering College, Gansu Agricultural University, Lanzhou, Gansu 730070, China | | ZHAO Jingtao | Mechanical and Electrical Engineering College, Gansu Agricultural University, Lanzhou, Gansu 730070, China | | ZHAO Xining | Institute of Water and Soil Conservation, Ministry of Water Resources, Chinese Academy of Sciences, Yangling, Shaanxi 712100, China | | CAI Yaohui | Institute of Water and Soil Conservation, Ministry of Water Resources, Chinese Academy of Sciences, Yangling, Shaanxi 712100, China | | FENG Quan | Mechanical and Electrical Engineering College, Gansu Agricultural University, Lanzhou, Gansu 730070, China |
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| Abstract: |
| Leaf water content is a key indicator for assessing crop water status and optimizing irrigation decisions. To explore a rapid and accurate method for obtaining potato leaf water content based on UAV hyperspectral image data, high\|resolution spectral data of potato canopy were acquired through low\|altitude flight. Continuous wavelet transform was applied to decompose the original spectra at multiple scales, and the correlation between wavelet coefficients at each scale and leaf water content was analyzed. The potential of using CWT spectra to construct vegetation indices for rapid estimation of potato leaf water content was investigated. The competitive adaptive reweighted sampling method was employed for spectral feature selection, combined with partial least squares regression, random forest, and extreme gradient boosting to achieve high\|precision estimation of leaf water content. The results showed that from scale 1 to scale 8, the correlation between wavelet coefficients and potato leaf water content first increased and then decreased, with medium decomposition scales being optimal for improving correlation (the absolute value of the correlation coefficient increased from 0.59 to 0.84). The empirical vegetation indices constructed from wavelet coefficients showed amplified correlation with water content at specific scales, and models built using optimal scale combinations of each index outperformed original spectral vegetation indices. For estimating leaf water content based on single\|scale wavelet coefficients, the XGBoost model established after CARS screening of scale 5 CWT spectra performed best (R2=0.88, RMSE=1.235%). Feature fusion from scale 5 and scale 7 further improved the estimation model performance (R2=0.90, RMSE=1.145%). In conclusion, monitoring leaf water content using multi\|scale features selected by CWT combined with the CARS method provides better estimation accuracy, offering an effective approach for precision water management in potato cultivation. |
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