@article{Zhou2025, 
author = {Jiulin Zhou and Zhongyu Jin and Juchi Bai and Shilong Li and Shuai Feng and Weixiang Yao and Tongyu Xu and Fenghua Yu},
title = {Research on Fertilization Decision-Making Methods for Rice Tillering Stage Based on Multi-Source Remote Sensing Data},
year = {2025},
journal = {Tsinghua Science and Technology},
keywords = {Multi-source remote sensing, Nitrogen deficiency, Fertilization decision-making, Intelligent optimization algorithms, Rice},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010124},
doi = {10.26599/TST.2025.9010124},
abstract = {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.}
}