Data-driven estimation of lettuce biophysical traits using multidimensional sensing

Accurate monitoring of leafy vegetable crops is essential to evaluate plant health, growth, yield, and quality, yet conventional methods based on manual measurements are labor-intensive and error-prone. This study proposes a data-driven framework for automated in-field monitoring of a lettuce crop based on multidimensional data acquired by a ground platform under various field conditions. Specifically, an advanced perception system is developed, including imaging and localization sensors to capture high-resolution visual, structural, and georeferenced information on the crop. An image processing pipeline is then proposed using zero-shot learning for plant segmentation, followed by 3D phenotyping techniques based upon computational geometry to automatically estimate plant biophysical traits, thus minimizing human input. An experimental trial conducted in a test field in Bari, Italy, between April and May 2025 validated the approach against manual and laboratory estimations. The results demonstrate strong correspondence between automated and reference measurements with a Pearson correlation coefficient r > 0.9 for key traits, confirming the potential of the framework. The influence of different nitrogen levels on the growing cycle is also evaluated, showing that the proposed system may provide a useful tool for decision support in lettuce crop monitoring and management.