UAV-Based Spectral Monitoring of Crop Growth Status: Techniques, Equipment, and Challenges

Rapid and accurate acquisition of crop growth status information is fundamental to crop condition diagnosis, water and nutrient management, and variable-rate field operations. With its operational flexibility at low altitudes, high spatial resolution, and capacity for rapid, non-destructive data acquisition, unmanned aerial vehicle (UAV)-based spectral monitoring has become a key tool for assessing the crop growth status. By characterizing within-field variability in crop growth and nitrogen status, UAV-based monitoring can support the generation of fertilizer prescriptions and variable-rate fertilizer application by agricultural machinery. Nevertheless, illumination variability, changes in platform attitude during flight, and interference from canopy backgrounds reduce the robustness of monitoring, while processing delays and difficulties in integration with variable-rate field operations remain major limitations. The spectral responses and scale effects, imaging and non-imaging spectral sensing, multi-sensor synergistic sensing, spectral data processing, and the retrieval of crop growth parameters are reviewed herein; the authors also summarize commonly used UAV platforms and representative commercial sensors. UAV-mounted imaging spectral sensors provide detailed spatial information but generate large datasets and require complex workflows that typically rely on offline processing, limiting the timely use of monitoring results in variable-rate field operations. UAV-mounted non-imaging spectral sensors enable faster processing but provide footprint-averaged measurements with limited spatial detail; they remain susceptible to environmental interference. Multi-sensor synergistic sensing provides complementary information pertaining to the canopy structure and physiological status but requires more complex payload integration and data co-registration. The present review further outlines future directions, including integrated onboard sensing, computing, and storage; multi-sensor data fusion; and multimodal retrieval and spatial mapping. These developments aim to reduce processing latency and interference during field observations, align monitoring outputs with variable-rate application units, and enable the development of lightweight monitoring equipment and the generation of prescription maps.