UAV-based precision nitrogen management balances agronomic, economic, and environmental multi-objective benefits in rice production

Context

Rice is a staple cereal crop in China, and nitrogen (N) is a key nutrient for its growth and development. Precision N management using remote sensing is critical for food security and sustainable agriculture. Although unmanned aerial vehicle (UAV)-based multi-spectral remote sensing has been increasingly applied to rice N monitoring, existing studies still face limitations in diagnostic accuracy, adaptability across cultivars and regions, and validation for large-scale applications. Therefore, UAV-based precision N management strategies tailored for rice require further investigation.

Objectives

(1) Develop UAV-enabled N topdressing diagnosis and regulation methods suitable for intra-field and field scales; (2) Evaluate agronomic, economic, and environmental outcomes of these regulation approaches; (3) Identify an optimized fertilization strategy that balances multi-objective benefits.

Methods

Multi-year experiments (2017–2023) in Xinghua City covered multiple cultivars and N rates. UAV multi-spectral imagery from key stages, combined with agronomic and temperature data, supported topdressing diagnosis through the N nutrition index (NNI) and accumulated N deficit (AND). We developed and validated RF-Variable (intra-field, prescription-map based) and RF/CNN-Optimized (field-scale, multi-objective) regulation approaches. Validation was conducted at Xinghua Station, Zhuhong Farm, and Zhouzhuang Farm (2022–2023).

Results

Direct inversion achieved robust accuracy (NNI: R²=0.62, RMSE=0.20; AND: R²=0.61–0.64, RMSE=20.79–22.40 kg ha⁻¹). Both approaches proved superior to conventional fertilization, increasing the proportion of rice plants with N-suitable status by 22.07 %–67.01 %. At the intra-field scale, RF-Variable reduced the coefficient of variation of NNI by 9.29 %–15 % and improved N agronomic efficiency (NAE) by 19.02 %–20.11 %. At the field scale, CNN-Optimized achieved a balanced performance across agronomic, economic, and environmental objectives.

Conclusions

Integrating UAV multi-spectral diagnosis with scale-appropriate regulation enables actionable, data-driven N topdressing in rice. RF-Variable is effective for pixel-level, variable-rate application; CNN-Optimized is suitable for field-level decision optimization where uniform application is required.

Implications

The proposed framework links remote sensing diagnosis to implementable prescriptions, advancing productive, cost-effective, and environmentally sustainable rice N management in alignment with global sustainability goals. Further multi-site, multi-season deployment will facilitate broader adoption and policy/extension integration.