With the rise of precision agriculture, efficiently identifying fruit tree locations and optimizing operational paths have become increasingly challenging. To address these issues, we developed an integrated path automation system that leverages the collaboration between unmanned aerial vehicles (UAVs) and ground robots. The system captures multi-angle images of fruit trees using UAVs, which are then processed by the Multi-MiniGPT-v2 model, which is an intelligent multi-agent system incorporating MiniGPT-v2 and MetaGPT, to determine tree coordinates and assess fruit ripeness from both top-down and side views. This coordinate data is input into the Adaptive Reflective Evolution (AREvo) model, which utilizes the large model and Reflective Evolution algorithm for automatic path planning and generates optimized robot paths. Experimental results indicate that this method significantly enhances the operational efficiency of orchard robots, improves the accuracy of fruit tree status detection, and reduces costs. This technology alleviates manual labor, boosts agricultural productivity, and optimizes resource utilization, providing strong support for advancing precision agriculture.