Wheat pests and diseases threaten food security, but field management still lacks intelligent systems that can connect visual diagnosis with executable treatment recommendations. This study proposes WDPG, a knowledge-enhanced multi-agent framework that integrates a three-layer heterogeneous knowledge graph with a source-traceable prescription knowledge base, and combines DSPy-based diagnostic prompt optimization, DeepResearch-based evidence completion, and agentic prescription verification. The system takes wheat images or symptom descriptions as input and coordinates Diagnostic, Prescription, and Critic agents to generate structured prescriptions with traceable evidence, dosage, application method, and pre-harvest interval. On a 601-sample held-out test set, the integrated WDPG system achieved an end-to-end full-prescription accuracy of 0.9218 and demonstrated better overall performance than GPT-5.5 and GLM-4.6V for wheat pest and disease diagnosis and prescription generation. These results support the effectiveness of the integrated WDPG framework for structured and source-grounded wheat pest and disease diagnosis and prescription generation.