Artificial intelligence (AI) and its applications in precision agriculture, the proliferation of artificial intelligence (AI) in precision agriculture, has made it possible to realize novel methods to increase crop yield, minimize resource utilization, and make informative decisions. Nevertheless, a great portion of existing AI models work as “black boxes” and offer little transparency and interpretability to its end-users, e.g., farmers, agronomists, and policy makers. This lack of explainability is counterintuitive to promotion and adoption of AI-driven decision support systems to be tried and true in situations with high stakes of human and economic loss for suboptimal recommendations. This section introduces a detailed discussion of Explainable AI (XAI) methods in a precision agriculture focused context. It addresses both post hoc and intrinsic techniques including SHAP, LIME, Grad-CAM, fuzzy logic rule-based systems, and interpretable neural networks. The chapter further discusses important applications on yield prediction, disease scouting, and crop advisory system based on real-world case studies, where transparency is indeed critical. Furthermore, it explores objective functions to measure the quality of explanation and presents a framework designed to incorporate human-centric feedback into the iterative development process of AI. We discuss how AI is examined and influenced and the focus is on how AI decisions are increasingly made more interpretable, accountable, and aligned with legal and ethical frameworks. This chapter argues how explainability is an essential requisite for development and deployment of AI-based systems in agriculture, creating trust and sustainability.