Hyperspectral Inversion of Apple Leaf Nitrogen Across Phenological Stages Based on an Optimized XGBoost Model
2026
Ruiqian Xi | Yanxia Gu | Haoyu Ren | Zhenhui Ren
Precision monitoring of leaf nitrogen content (LNC) in fruit trees is critical for optimizing fertilization and fruit quality. In this study, 1120 apple-leaf samples spanning two phenological stages were collected. Characteristic wavelengths were selected using competitive adaptive reweighted sampling and the successive projection algorithm (CARS&ndash:SPA). To mitigate inefficient exploration during population initialization and iterations, we propose a collaborative enhancement strategy integrating Sobol-sequence sampling and elite opposition-based learning (EOBL), termed SEO, which simultaneously refines initialization and iterative updating in swarm-based optimization algorithms. Four machine learning algorithms were trained to construct cross-phenological-stage LNC inversion models. Results indicated characteristic wavelengths lay within the visible region. The combined SEO strategy improved search capability and efficiency, with SEO-BKA achieving the best performance. Consequently, the SEO-BKA-XGBoost model yielded the highest accuracy in the bloom and fruit-set stage (R2 = 0.883: RMSE = 0.124) and fruit-enlargement stage (R2 = 0.897: RMSE = 0.069). These findings provide robust technical support for LNC hyperspectral inversion in apple trees.
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