Autonomous precision agriculture systems increasingly rely on artificial intelligence for real-time decision-making in crop management, irrigation scheduling, pest control, and resource allocation. However, the inherent opacity of deep learning and ensemble models creates significant barriers to farmer trust, regulatory compliance, and system accountability. This article examines explainable AI (XAI) frameworks designed to enhance transparency and interpretability in autonomous agricultural operations. We analyze model-agnostic techniques including LIME, SHAP, and attention mechanisms, alongside domain-specific approaches for agricultural decision contexts. The integration of XAI with autonomous robotic platforms, sensor networks, and decision support systems is explored through case studies in crop health monitoring, variable-rate application systems, and predictive disease management. Results demonstrate that XAI implementations improve farmer acceptance by 34-42% while maintaining predictive accuracy above 89% across multiple agricultural tasks. Challenges including computational overhead in edge devices, real-time explainability constraints, and standardization of explanation formats are critically assessed. We propose a layered XAI architecture that balances model performance with interpretability requirements for different stakeholder groups. Future directions emphasize federated learning with built-in explainability, causal inference frameworks, and regulatory-compliant transparency mechanisms for autonomous agricultural AI systems.