The analysis of plant characteristics using computer vision techniques is dominated by the PlantCV library. However, the nature of the library makes it difficult to rapidly construct, edit, and debug one’s workflow for processing images across different growing environments. A Python and R codebase was created that enables the modular development of PlantCV pipelines for lettuce segmentation and analysis. Images for processing were sourced from a custom module equipped with an Intel RealSense Camera and a Raspberry Pi4 used at Cornell University. Graphs were produced from the resulting CSV information obtained from the pipeline outputs; the trends seen in each were consistent with expectations for a linear-like increase over the growing cycle. Significant variability was observed over the course of a single day for trait readings such as leaf surface area; therefore, increased collection frequency may be useful in order to get a better average for estimating the true value.