<![CDATA[Drone-based monitoring systems have emerged as an effective solution to improve the efficiency of large-scale agricultural land surveillance, particularly in oil palm plantations. This study proposes an artificial intelligence (AI)-based simulation using dual drones to map optimal and distributed flight paths. The simulation considers the random wind effect on trajectory accuracy using a grid-based waypoint approach across the plantation area. The results show that both drones successfully completed the land inspection mission with an average wind-induced deviation of ±0.14 meters, indicating system stability under dynamic environmental conditions. Drone 1 covered a total distance of 9244.10 meters, while Drone 2 covered 10602.47 meters. A 3D trajectory visualization illustrates that the path deviations remained controlled. This research provides a foundation for developing more adaptive and efficient autonomous drone systems in the context of smart farming.]]>
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