Lab-to-Field Correlation Dataset: 142 Nozzle Types Across 7 Crop Systems

This dataset addresses a critical gap in precision agriculture: the disconnect between idealized laboratory nozzle ratings and actual field behavior. Nozzles are traditionally characterized in ISO-standardized lab settings (e.g., using laser diffraction or phase Doppler interferometry) at fixed pressures and distances—but field performance is modulated by canopy density, wind, humidity, boom height, forward speed, and spray volume. The dataset was compiled through coordinated multi-year trials across >30 geographically diverse research sites, where each of the 142 nozzles (spanning flat-fan, hollow-cone, air-induction, pre-orifice, and twin-fluid types) was tested under replicated field conditions representative of the seven crop systems. For each test, synchronized measurements included high-resolution droplet spectra (via optical array sensors), deposit quantification (using fluorescent tracers and image analysis), off-target drift (using passive collectors and tethered balloons), and biological efficacy (e.g., weed control or disease suppression). Statistical models—including mixed-effects regression and machine learning–enhanced response surface modeling—were used to derive correction factors and correlation coefficients linking lab metrics (e.g., VMD at 40 psi) to field outcomes (e.g., % leaf coverage in dense soybean canopies). As a result, the dataset functions not merely as a lookup table but as a predictive framework supporting digital spray decision support tools, EPA pesticide label refinement, and ISO/ASABE standard updates.