NDRE-Guided Side-Dressing Tool for Corn Nitrogen Management
Data-driven approach using normalized difference red edge (NDRE) imagery and on-farm trial data to improve corn side-dressing nitrogen recommendations, boosting both economic returns and environmental sustainability.
Overview
This solution leverages satellite-derived normalized difference red edge (NDRE) measurements alongside extensive on-farm precision nitrogen trial data to improve in-season side-dressing recommendations for corn. By identifying the optimal window between the V4 and V8 growth stages to capture NDRE imagery, the approach aims to forecast yield response to nitrogen before permanent yield loss occurs. The result is more precise nitrogen application that increases farmer profitability while reducing environmental impact from over-application.
Technical specifications
Key features:
- Integration of NDRE remote sensing data with multi-year on-farm precision nitrogen trial datasets (over 100 trials from 2016–2020)
- Use of machine learning and geospatial models to analyze how NDRE timing influences optimal side-dressing rates
- Incorporation of complementary field data layers including electroconductivity, satellite imagery, topography, and soil sampling
- Evaluation of side-dressing timing at V4, V6, and V8 growth stages to identify the latest effective application window
- Established experimental infrastructure enabling rapid design and deployment of new field trials
Planned validation: Twenty collaborative on-farm side-dressing trials generating field data, with multiple NDRE measurements following pre-plant nitrogen application and randomized side-dressing rates at different growth stages.
Technology readiness level
The research team has extensive prior experience estimating corn yield response to nitrogen and identifying economically optimal nitrogen rates through over 100 on-farm precision nitrogen experiments. While past trials did not specifically test NDRE-guided side-dressing, the existing datasets and infrastructure provide a strong foundation. The next phase involves new field trials and ML-based analysis to validate whether NDRE measurements before permanent yield loss can explain variation in yield response, advancing the technology from concept toward field-validated decision support.
About University of Illinois, Urbana-Champaign
The University of Illinois Urbana‑Champaign is a flagship public research university with large‑scale research capacity and a broad academic portfolio. An on‑campus Research Park co‑locates corporate R&D teams and startups with faculty, while the National Center for Supercomputing Applications provides advanced computing and data capabilities for collaboration. Integration with a regional health system and an engineering‑based college of medicine enables clinical translation, and a long‑standing extension network links campus innovation to partners statewide. Research is supported by competitive federal funding from NSF, NIH, DOE, USDA, and DoD. A technology transfer office streamlines IP, licensing, and startups, complemented by incubators and prototyping in the Research Park.
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