Over the last few decades, the agricultural industry has witnessed significant advancements in autonomous systems, primarily aimed at improving efficiency while reducing environmental impact. The critical role of complete coverage path planning cannot be overstated in this context. It involves determining an optimal path for tasks such as harvesting, mowing, and spraying, taking into account various factors like land topography, operational requirements, and robot characteristics. Our previous approach introduced a tree-based exploration method to generate potential solutions, coupled with an optimization process to select the best ones, considering field complexities and robot characteristics. Yet, despite its strengths, it had certain limitations, notably in computational time and number of examined driving directions. In this paper, we present a novel hybrid method that combines the comprehensive coverage benefits of our original approach with the computational efficiency of the Fields2Cover algorithm. Besides combining our previous approach and Fieds2Cover strengths for optimizing coverage area, overlaps, non-working path length and overall travel time, it significantly improves the computation process, enhances the flexibility of trajectory generation. It also takes into account the working trajectory inclinations for more advanced optimization to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public dataset. This dataset includes both 2D and 3D representations of thirty agricultural fields located in France. This resource not only illustrates the effectiveness of our approach but also provides an invaluable data for future research in complete coverage path planning within the context of modern agriculture.