Estimation of corn nitrogen demand under different irrigation conditions based on UAV multispectral technology

Abstract

Integrating water and nitrogen (N) management is critical to addressing contemporary challenges in agricultural development. This research explored using multispectral sensors mounted on unmanned aerial vehicles (UAVs) to monitor N demand via the normalized difference red-edge (NDRE) vegetation index and consequently schedule fertigation. The experiment included eight treatments with four fertilizer levels under both excessive and full irrigation. The four fertilizer levels comprised: high reference treatment based on commercial lab soil tests, sensor-based treatment triggered by an NDRE saturation threshold of 0.95, deficit treatment with base rate at pre-plant and side-dress, and a control treatment without any N application. The performance of each treatment was evaluated through a comprehensive comparison of yield, water productivity (WP), and nitrogen use efficiency (NUE). The sufficiency index (SI) of sensor-based treatment plots reached a threshold of 0.95, allowing spatially variable adjustment of N application for optimal yield with reduced total N input. Reducing N fertilizer in sensor-based treatments resulted in a substantial reduction of 50 %-60 %, though it led to a yield loss up to 12 %. However, NUE parameters such as partial factor productivity, agronomic efficiency, recovery efficiency, and physiological efficiency improved with sensor-based treatments, alongside reduced N leaching. Combining sensor-based treatment with full irrigation demonstrated the best ecological return, showing relatively lower yield reduction but significant improvements in NUE and WP. Further research into economic returns, saturation threshold algorithms for SI, adaptability to diverse environments, and virtual saturation reference is recommended for the widespread adoption of UAV-based N split management among growers.