Few alternatives are available to the sugarcane sector for monitoring crop productivity. However, in recent years, research has been dedicated to developing methods ranging from estimation based on engine parameters to using sensors and artificial intelligence. This study aims to present a new tool for monitoring productivity applied to sugarcane cultivation, which utilizes a volumetric optical sensor, in contrast to other methods already used for this measurement, and is recently being introduced to the market. The monitoring system is presented based on a database generated during the harvest of a 16 ha area. The data provided by the monitor were filtered, leaving a high volume (55,742 points) yet to be analyzed. An exploratory, statistical, and agronomic analysis followed to explore the functionalities that the new technology can offer in decision-making. Two methods of application to these data are presented to assess their effectiveness in mapping productivity, primarily considering the high data collection density of the system (1 Hz) associated with high positioning accuracy (± 3.50 cm). The technology demonstrates high potential for use in precision agriculture. Future work with this monitor may test its use for recommending fertilizers at varied rates, identifying faults, among various possible applications.