Log loading constitutes a key operation in timber harvesting, and despite the recent spike of interest in introducing automation to the forestry sector, efficient and intelligent grasping of logs remains unresolved. This paper presents a grasp planning pipeline that relies on the identification of logs' characteristics and pose in the environment of a log-loading machine, to generate high quality grasps. The proposed pipeline involves replicating identified logs in a virtual environment where grasp planning is carried out by using a convolutional neural network and a virtual depth camera. The network relies solely on depth information and the virtual camera can be positioned at a strategically selected location or to follow a certain trajectory to enhance exposure of the logs, all this without having to move the log-loader's crane. The grasp planning pipeline is evaluated through simulated grasping trials and experiments on a large-scale log-loading test-bed with several configurations of wood logs ranging from a single to multiple logs. The grasp planning pipeline proved to be successful with a grasping rate of 98.33 % in the simulated trials and 96.67 % in the experimental trials. The grasp planner was able to overcome log characterization and localization uncertainties, thus allowing the log-loader to pick individual logs, and multiple logs at once when possible.