This paper introduces an integrated system for autonomous crop field scouting, which incorporates novel algorithms for crop row identification and multirobot (ground and aerial) task allocation. The system utilizes a flexible team of aerial and ground robots, coordinated by a central computer that manages both the user interface and the back-end software that handles crop row detection and task allocation algorithms. The user interface allows users to initiate the scouting process by selecting target areas on an aerial map and to monitor and control the process in real time. Autonomous crop row detection is achieved through a Geographic Information System (GIS)-based method that processes aerial orthomosaic images of the target field to generate geo-coordinates of in-between crop row edges. This information is then input into the task allocation algorithms, which determine the number and types of robots required and assign them to specific locations to cover the field optimally. The adaptable architecture accommodates different types and numbers of robots, allowing it to scale to farms of varying sizes. Simulations and field tests were performed to validate system-level integration.
Wei et al. (Mon,) studied this question.
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