Research on Fertilization Decision-Making Methods for Rice Tillering Stage Based on Multi-Source Remote Sensing Data

Nitrogen is a vital nutrient that affects rice growth and yield, playing a key role in photosynthesis, protein synthesis, and carbon-nitrogen metabolism. Effective fertilization decisions depend on nitrogen nutritional status. Traditional methods rely on field sampling and biomass measurement, which are inefficient and lack real-time data. This study proposes a nitrogen nutrition diagnosis method using UAV remote sensing technology combined with a critical nitrogen concentration dilution curve based on Leaf Area Index to guide fertilization during the rice tillering stage. UAV-acquired multi-source remote sensing data, including visible light and hyperspectral images, are used to construct LAI inversion models and nitrogen concentration inversion models optimized by ZOA-KELM and DBO-KELM, respectively. A critical nitrogen concentration dilution curve for rice based on LAI (R²=0.87) was established. Using this method, nitrogen deficiency was calculated, guiding fertilization decisions. Compared to traditional methods, this approach reduces fertilization by 7.8% while ensuring stable yield. In conclusion, fertilization based on the LAI-based nitrogen dilution curve provides an efficient solution for precision fertilization in modern agriculture.