A Nitrogen Application Decision-Making Scheme for Tobacco Growth Based on UAV Multispectral Imagery

Nitrogen is a critical nutrient for tobacco growth, substantially impacting leaf development and photosynthesis. Excessive nitrogen negatively affects tobacco quality, while insufficient nitrogen lowers yield per unit area. Traditional fertilization methods often rely on farmers’ experience, making it difficult to precisely meet the specific nitrogen requirements of tobacco. This study proposes a recommended nitrogen application index (RNAI), derived from multispectral imagery obtained by unmanned aerial vehicles (UAVs), to enable precise nitrogen application and improve tobacco yield. Key agronomic traits—including leaf area index (LAI), leaf biomass (LB), chlorophyll content (LCC), and leaf nitrogen content (LNC)—were used to construct RNAI through the entropy weight method (EWM). The Pearson correlation coefficient and recursive feature elimination (RFE) were employed to identify vegetation indices (VIs) from UAV imagery that significantly correlated with each agronomic trait. Estimation models for LAI, LB, LCC, and LNC were optimized using random forest (RF) and extreme gradient boosting (XGBoost) algorithms, with XGBoost consistently outperforming RF for all indicators. Based on RNAI, a nitrogen application decision-making scheme for tobacco growth was developed. The RNA decision-making model, informed by RNAI fitting curves, guided nitrogen application rates and produced a decision-making map for nitrogen management in tobacco fields. Applying this map reduced pure nitrogen usage by 3.71 kg/ha, increased yield by 54.56 kg/ha, and enhanced economic benefits by 2.58% while improving the uniformity of the tobacco population. The RNA decision-making model established in this study provides a robust scientific framework for precise nitrogen management in tobacco cultivation.