<p>Advances in UAV remote sensing have prompted the exploration of phenotypic traits, such as canopy area, for timely yield assessments. However, conducting UAV-based yield assessments in large commercial fields under variable weather conditions remains challenging. We present an image analysis framework for the timely assessment of commercial tomato production in a raised bed plasticulture (RBP) system. Using a color-based segmentation method, we calculated the canopy area (crop canopy and weeds) to estimate yield. This approach was applied to evaluate common field-scale challenges, including fluctuating weather conditions (e.g., wind and lighting), weed interference, and crop maturity, all of which affect yield estimation accuracy in large commercial fields. Results indicate that the effectiveness of color-based segmentation depends strongly on orthomosaic image quality. Therefore, adhering to best-practice recommendations for image acquisition and performing thorough preprocessing are essential when using these techniques for yield prediction . Regression analyses support the use of calculated canopy area to predict the total tomato yield grown in commercial RBP systems. Our findings suggest that from 10 weeks after transplanting to harvest, tomato plants in RBP systems in South Florida are mature enough to reflect their production potential. However, weed interference increases as harvest approaches. The results demonstrate the potential of the proposed approach to provide insights into yield responses to phosphorus (P) fertilization. Incorporating soil nutrient input data into regression models further improved yield prediction accuracy. Overall, canopy-based yield prediction offers a promising, cost-effective approach for early yield estimation and can be applied in agronomic research to support sustainable fresh-market tomato production in RBP systems.</p>