aceWheat yield prediction using machine learning, proximal sensing, nitrogen and climate data for Oklahoma
PURPOSE: Accurate in-season yield prediction is a practical prerequisite for informed nitrogen (N) management decisions in winter wheat production, yet no rigorous framework has been established and tested against the tools producers currently rely on. Our study developed and evaluated machine learning (ML) models for pre-application wheat yield prediction to support in-season top-dress N management in Oklahoma. METHODS: Four ML algorithms were evaluated using 57 site-years of multi-source data from rainfed Okla…
A machine learning model using sensor, soil, nitrogen and free climate data predicted wheat yield before nitrogen application with R² of 0.81, versus 0.12 for the INSEY benchmark.
The evidence covers rainfed Oklahoma environments, so performance elsewhere is not established by the abstract.
Evidence
- Peer-reviewedPrecision agriculture2026-10-01
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Truvace Impact Record TRV-2026-1399, v1: “Wheat yield prediction using machine learning, proximal sensing, nitrogen and climate data for Oklahoma.” Truvace, 2026-10-15. /record/TRV-2026-1399 (accessed at citation time). sha256 3b2cff5e0034afd8…
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