The integration of computer vision techniques with Internet of Things (IoT) technologies has enhanced precision agriculture, offering solutions for plant phenotyping and growth monitoring. This study introduces a novel automated, high-throughput phenotyping system that uses off-the-shelf hardware (Intel RealSense D435, Raspberry Pi) to capture RGB-D images every 10 minutes, generating 3D point clouds of lettuce under controlled greenhouse conditions. We employed the Segment Anything Model (SAM) and FastSAM for precise segmentation of individual lettuce plants in high-density arrangements, overcoming challenges posed by overlapping foliage and complex backgrounds, and significantly outperforming traditional methods like binary thresholding, thus providing empirical justification for their selection. By mapping 2D segmentation masks onto corresponding 3D point clouds, we achieved accurate measurements of key phenotypic traits such as plant height, area, and volume. The system captures images every 10 minutes, enabling continuous, non-destructive monitoring of crop growth and providing insights into plant growth dynamics. We also implemented and validated machine learning models to predict fresh weight from phenotypic data, enhancing yield prediction accuracy. Our results show high accuracy in phenotypic trait measurements, with strong correlations to manual measurements for Rex and Rouxai lettuce cultivars. The study highlights the distinct growth patterns between these cultivars, underscoring the importance of tailored phenotyping approaches to optimize growth conditions. By addressing the limitations of existing phenotyping methods, our work contributes to the advancement of precision agriculture technologies, offering a scalable and efficient solution for real-time crop monitoring with potential applications across various crops and growing conditions.