Assessing forage mass using smartphone imagery for cereal rye under grazing conditions in Nebraska

Abstract Managing grazing efficiency requires reliable and practical tools to estimate forage mass. Traditional methods like clipping, though accurate, are time consuming and labor intensive. In this study, two imaging tools, Canopeo and the Crop Canopy Image Analyzer (CCIA), were evaluated for their efficacy in estimating the forage mass of cereal rye ( Secale cereale L.) using smartphone images collected under rotational grazing conditions in Nebraska, USA. Over 2 years, 400 forage samples were collected. Forage images were taken using smartphones before clipping, and canopy cover was estimated from these images using Canopeo and the CCIA tool. Regression analysis was performed to compare the canopy cover obtained using these image tools with the measured dry forage mass. Quadratic regressions were developed using 80% of the data. Validation of the predicted and measured forage mass was conducted using the remaining 20% of the data. Both tools showed strong predictive potential, with the CCIA being relatively simpler to operate and exhibiting slightly superior performance ( r 2 = 0.78; RMSE = 362 lb DM ac −1 ) compared with Canopeo ( r 2 = 0.72; RMSE = 446 lb DM ac −1 ). Our results indicated that canopy cover relative to forage mass showed greater variability at more mature phenological stages, plausibly attributed to stem elongation rather than leaf mass accumulation. These findings suggest that both tools are suitable for use during the vegetative stage, when grazing typically occurs, but may be less accurate at advanced maturity stages. Nondestructive image‐based approaches offer a practical, time‐saving alternative to traditional methods and hold promise for supporting producers and scientists in making timely grazing decisions.