Remote sensing plays a pivotal role in Precision Agriculture by providing relevant information regarding crop diseases and deficiencies, and fruit counting, which can derive yield estimation. Unmanned Aerial Vehicles (UAVs) offer a great opportunity for Remote Sensing in order to acquire high-spatial-resolution data. Nevertheless, challenges arise when it comes to accurate object detection in crops that present leaf-occlusion. Moreover, there is a limitation while capturing information with only one UAV since the flight time is restricted by the battery level. However, using multiple UAV can provide an augmented agricultural-scene understanding to address occlusion problems. This study presents a novel approach to address these challenges by enhancing UAV path planning specifically designed for fruit detection in woody crops trained in vertical trellis, considering the biophysical environment of the field. The experiments were carried out in a vineyard (Vitis vinifera cv. Loureiro) located in the North-West of Spain. The proposed method implements the Ant Colony Optimization (ACO) algorithm to optimize data collection and enable single and multiple UAVs to fly synchronously while ensuring a safety distance between platforms and achieving efficient coverage of the agricultural area. In order to enhance data collection for fruit detection purposes, the methodology incorporates a two-flight strategy. The first flight (with 1 UAV) serves as a survey to comprehend and analyze the crop structure and environmental conditions of the agricultural field. In this step, the field is discretized as waypoints (areas to visit) and forbidden areas, which include also areas without agronomic interest. Further, the second flight (with n UAVs) is executed following the optimal path between waypoints. Also, it enhances image acquisition by considering multiple angles, effectively mitigating the adverse effects of partial leaf-occlusion. The results obtained from this study highlight that ACO is able to generate optimal and safe routes within the field by covering the whole agricultural area while flying one or multiple UAV platforms. Moreover, it shows potential to solve partial leaf-occlusion for fruit identification.