From a Precision Agriculture perspective, it is important to detect field areas where variabilities in the soil are significant or where there are different levels of crop yield or biomass. Information describing the behavior of the crop at any specific point in the growing season typically leads to improvements in the manner the local variabilities are addressed. The proper use of dense, in-season sensor data allows farm managers to optimize harvest plans and shipment schedules under variable plant growth dynamics, which may originate from soil spatial variability and management conditions. Sensing of crop architectonics has been used as a diagnostic tool in this context. Moving from the subjective visual estimation of farm workers to automated sensing technologies allows for improved repeatability and savings in cost, time, and labor. The goal of this paper is to report on the evaluation of a prototype sensor system embedded in a portable, low-cost instrument for green vegetable production. The prototype system is currently in its second iteration, featuring improvements for issues found in a previous experiment. The system involves circular scanning of crop canopies to identify crop biomass yield using laser triangulation. The results of these scans are height profiles along an angular position from 0° to 360°, which are the input for the biomass estimation. Two approaches for processing the laser-based height profiles are discussed: regression of profile-representative features and inference of a canopy density function. An experiment was conducted in a spinach field of a commercial farm in Sherrington, Quebec, Canada. The coefficient of determination (R2) for regression between measured and predicted biomass was 0.78 and 0.94. The root mean square error (RMSE) was in turn 4.18 and 2.16 t/ha. The results indicate that the developed sensor system would be a suitable tool for rapid assessment of fresh biomass in the field. Its application would be beneficial in the process of optimizing crop management logistics, comparing the performance of different varieties of crops, and detecting potential stresses in a field environment.