Agricultural Robotics

Agricultural robotics studies machines that monitor crops and carry out field and greenhouse work, aiming to make farming more precise and reduce labor and resource use. Systems combine cameras, depth sensors, LiDAR, and satellite or inertial data for crop perception and localization, with manipulators and compliant grippers for harvesting, pruning, and targeted treatment. Common approaches include deep-learning vision, active sensing, SLAM, model-predictive control, and reinforcement learning, increasingly tested in simulation, field datasets, and real farm trials. Robots can handle specific tasks, but reliable operation across changing light, seasons, terrain, and occlusion remains difficult, as does transferring simulated or laboratory performance to safe, selective work in the field.