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Real-time in-season variable rate corn fertilizer application using canopy sensors and UAVs
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2026 ASABE Annual International Meeting 2600097.(doi:10.13031/aim.202600097)Authors: Kalynn Rose Meyer, None
Keywords: Active canopy sensing, corn nitrogen management, multispectral real-time sensing, nitrogen recommendation algorithm, UAV, variable-rate fertilizer.
Abstract. Tightening agricultural profit margins and increasing fertilizer costs have intensified the need for nitrogen (N) management strategies that reduce inputs while maintaining or improving crop productivity. This study developed and evaluated a unmanned aerial vehicle (UAV)-based active canopy sensing system designed to support in-season, demand-driven N application in corn (Zea mays L.) production. The system integrated active canopy reflectance sensing with rolling reference normalization framework to generate a Sufficiency Index (SI) and convert canopy status into spatially variable N recommendations using a sigmoid response function. Field experiments were conducted in irrigated corn under a replicated N rate structure spanning low to high treatments. UAV-derived recommendations were evaluated against observed yield response and a quadratic yield model to assess agronomic and economic performance. Results showed that simulated N management increased mean applied N by 13.1 kg-N ha-1 and improved predicted yield by 0.18 Mg ha-1 relative to quadratic-estimated response (p<0.001). Yield gains were concentrated in low- to moderate-N treatments, while high-N treatments showed minimal or no additional yield response, indicating diminishing crop responsiveness near the yield plateau. Economic analysis showed a modest overall increase in marginal net return (MNR) of $4.79 ha-1; however, this difference was not statistically significant (p=0.161). Treatment-level results revealed strong stratification, with substantial economic gains in low-N environments (up to +$148.60 ha-1) and neutral to negative responses in high-N treatments where fertilizer inputs exceeded the economically optimal range. The fitted economic response curve identified a maximum MNR at 138.9 kg-N ha-1 ($2511.10 ha-1). Overall, the UAV-based system effectively improved N management efficiency under N-limited conditions by aligning applications with crop demand. However, performance was constrained in high-input scenarios where additional N provided limited or negative economic return. These findings highlight the potential of UAV-integrated active canopy sensing for adaptive in-season N management while underscoring the need for improved economic constraint incorporation and multi-season validation to enhance system robustness and generalizability.
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