Modern agriculture is increasingly challenged by the need for precision, sustainability, and reduced human intervention. This work presents an autonomous robotic solution for phenotyping row crops, combining embedded sensors, onboard vision, and fuzzy logic. A differential-drive mobile robot was developed and tested in a 3D-modeled agricultural field, structured in crop rows. It is equipped with infrared sensors for detecting plant obstacles and a front-facing camera for capturing images for phenotypic analysis. To ensure smooth navigation while preserving the integrity of the crops, fuzzy logic is employed to manage sensor uncertainty and dynamically adapt the robot's movements. This approach enables effective autonomous exploration while avoiding damaging contact with plants. The results highlight the relevance of combining sensors and artificial intelligence in the context of smart agriculture, particularly for non-destructive tasks such as automated phenotyping. This work contributes to the advancement of agricultural robotics by demonstrating the potential of intelligent embedded systems in realistic simulated environments for future field applications.