Early yield estimation is essential for agricultural planning, localized management and decision-making in commercial areas, especially in view of the high spatial variability of sugarcane fields. However, traditional estimation methods are generally destructive, costly, and inefficient in accounting for this variability. In this context, the integration of remote sensing data, soil attributes and machine learning techniques p...