Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R² values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

Strawberries (Fragaria × ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.