Aiming to address the challenges of low accuracy and inefficiency in phenotypic data acquisition, a high-precision, low-cost method for lettuce phenotypic parameter extraction in controlled environments was developed. An unmanned ground vehicle (UGV) equipped with autonomous navigation, multi-modal sensing, and multi-view imaging was deployed for automated in-situ data collection. A phenotype analysis pipeline incorporating a random under-sampling algorithm was designed to enhance point cloud processing efficiency. Image segmentation and clustering algorithms were implemented to extract multi-dimensional features, including plant height, maximum width, vegetation indices, and texture indices. Pearson correlation analysis between these imaging features and fresh biomass measurements identified four key variables highly correlated with biomass prediction. Single-feature and multi-feature biomass estimation models were constructed by using a back propagation algorithm. Results showed that the phenotyping pipeline designed took an average of 0.41s to process a single frame of data from a 5000 downsampled point cloud. The estimation models for lettuce height and maximum width achieved R2 values of 0.79 and 0.77, with mean absolute percentage errors (MAPE) of 4.94% and 5.02%, respectively. Compared with other biomass estimation models, the hybrid model incorporating four feature variables (HWVD) showed optimal performance, achieving an R2 of 0.82, RMSE of 4.03g, and MAPE of 6.04%. This method can provide a rapid, accurate, and non-destructive solution for field-based phenotyping and serve as a robust framework for investigating additional phenotypic traits.