Estimation of leaf nitrogen content in fruit trees within Arid Oasis Orchards using UAV-based spectral-texture fusion.

backgroundLeaf Nitrogen Content (LNC) is an important indicator of the nutritional status of fruit trees. To facilitate precise nutrient management in orchards within Xinjiang's arid region, this study focused on walnut (Juglans regia L.), jujube (Ziziphus jujuba Mill.), and apricot (Prunus armeniaca L.) trees in the Weigan-Kuqa River Delta Oasis (Weiku Oasis) of southern Xinjiang (50 samples per species), and used Unmanned Aerial Vehicle (UAV) multispectral imagery to develop an optimal inversion method for LNC. Spectral indices and Texture Features (TF) related to LNC were extracted, including eight Two-Band Optimized Spectral Indices (2B-OSI), eight Three-Band Optimized Spectral Indices (3B-OSI), and eight TF. These were integrated with Random Forest Regression (RFR), Support Vector Regression (SVR), and Quadratic Polynomial Regression (QPR) models to assess their performance.

resultsThe RFR model integrating 2B-OSI, 3B-OSI, and TF achieved the best performance, with validation set Coefficient of Determination (R

conclusionsThe integration of spectral indices and TF based on UAV multispectral imagery can effectively achieve inversion of LNC in fruit trees in arid regions, with the RFR model showing the best performance. However, the relatively limited sample size and validation uncertainty should be considered when interpreting model performance. These findings can provide theoretical support and technical guidance for precise fertilization and nutrient management in orchards in the arid region of Xinjiang.