Farmland Segmentation and Multiple Agricultural Machines Cooperative Scheduling Method Under Sudden Disasters

With the rapid development of smart farm models, multi-machine collaborative operations are an important trend. Nevertheless, in the face of sudden disasters, the traditional multi-machine collaborative operations have unclear operation area allocations, simplistic path planning, and scheduling algorithms easily falling into the local optimal solution, resulting in low efficiency and inability to meet the task needs of urgent tasks. A multiple agricultural machine cooperative scheduling method based on farmland segmentation is proposed to solve these problems. The method first segmented large-scale farmland based on multiple machine task requirements to obtain location information for the segmented sub-regions. Next, to minimize the turning time in the headland, complete coverage path planning inside the field is planned to obtain the best coverage angle and the shortest operation time. Lastly, an optimal simulated annealing algorithm is employed to obtain the task allocation of agricultural machinery to minimize the longest working time. The simulation results show that the study has achieved optimization results. Under urgent tasks, the method can effectively schedule agricultural machine resources, ensuring task completion and generating a high-yield solution.