Estimating Nitrogen, Phosphorus, and Potassium Content in Pear Trees Using UAV-Based Multispectral Imagery

Abstract

Machine learning combined with multispectral remote sensing provides an efficient and non-destructive approach for monitoring fruit tree nutrient status. However, conventional machine learning methods have limited ability to capture complex spectral-feature interactions, while deep neural networks often suffer from overfitting under small-sample field conditions. This study developed a UAV-based multispectral framework for estimating leaf nitrogen (N), phosphorus (P), and potassium (K) contents in a commercial pear orchard. Five-band spectral reflectance data were used to generate 21 vegetation indices (VIs), and Pearson correlation analysis and recursive feature elimination (RFE) were applied for feature optimization. A Deep Forest (DF)-based framework and a gradient boosting enhanced variant (GB-DF) were developed and compared with convolutional neural networks (CNN), support vector regression (SVR), random forest (RF), and gradient boosting decision tree (GBDT). Results showed that GB-DF achieved the best performance for N, P, and K estimation, with R2 values of 0.6959, 0.7535, and 0.7216, and RMSE values of 0.7877, 0.1280, and 0.6059 g/kg, respectively. Moreover, GB-DF required only 6.35 s for training, being approximately twice as fast as GBDT and 5.7 times faster than CNN. The proposed GB-DF framework shows potential to improve nutrient estimation accuracy under limited-sample conditions and provides an effective solution for UAV-based nutrient mapping and variable-rate fertilization in precision orchards.