Vehicle‐Mounted Optical Sensing: An Objective Means for Evaluating Turf Quality

TLDR

Visual evaluation of turfgrass quality is subjective and requires experienced personnel. The study assessed the accuracy of optical sensing for turf quality, compared human and sensor consistency, and developed a predictive model linking sensor measurements to visual ratings. Researchers collected monthly optical reflectance (R and NIR) and visual evaluations of color, texture, and percent live cover on NTEP tall fescue and creeping bentgrass trials over 12 months, converting reflectance to NDVI. NDVI correlated closely with turf color, moderately with percent live cover, was independent of texture, and sensor-based ratings were more consistent than visual ones, with a generalized model reliably predicting NDVI from color and PLC, supporting optical sensing as a fast, reliable alternative.

Abstract

Visual evaluation of turfgrass quality is a subjective process that requires experienced personnel. Optical sensing of plant reflectance provides objective, quantitative turf quality evaluation and requires no turf experience. This study was conducted to assess the accuracy of optical sensing for evaluating turf quality, to compare the rating consistency among human evaluators and optical sensing, and to develop a model that describes a relationship between optically sensed measurements and visual turf quality. Visual evaluations for turf color, texture, percent live cover (PLC), and optically sensed measurements were collected on the National Turfgrass Evaluation Program (NTEP) tall fescue (Festuca arundinacea Schreb) and creeping bentgrass (Agrostis palustris Huds.) trials at Stillwater, OK. Measurements were made monthly for 12 consecutive months from June 1999 through May 2000. Red (R) and near infrared (NIR) reflectance were collected with sensors and converted to normalized difference vegetative indices (NDVI). The NDVI were closely correlated with visual evaluations for turf color, moderately correlated with percent live cover (PLC), and independent of texture. Measurements of turf color and PLC were evaluated more consistently with optical sensors than by visual ratings. Normalized difference vegetation index (Y) could be reliably predicted by the following generalized model for turf color (X) and PLC (Z): Y = B(0) + B(1)log10X + B(2)Z(3). Optical sensing provided fast, reliable turf assessment and deserves consideration as a supplemental or replacement technique for evaluating turf quality.