Precision Agriculture using mobile robot aimed to increase efficiency and quality of crops treatment and monitoring, since robot can produce better result in term of data quality and accuracy than manual treatment by human labor. Limited by technology development and complexity of agricultural environment, precision agriculture nowadays is still not fully-autonomous, but partially autonomous to handle more simpler - but need high accuracy - tasks. Some of them is mapping and monitoring task. This research proposed a method for generating map in simulated agricultural area using Simultaneous Localization And Mapping (SLAM), by generating grid-based/volumetric map using fine-tuned SLAM-Gmapping algorithm and combine it with properties/informations obtained from each detected crops/plants using fruit detection with visual sensor and tree location detection using 2D laser scanner sensor. Experiment conducted in simulation environment Gazebo and Robot Operation System (ROS) for scenario of simulated fruit mapping in apple farm, and give a good result with a good accuracy.