Adopting artificial intelligence (AI) tools and technologies may be favourable for the agriculture sector in addressing food security and sustainability. As AI gains popularity, there are clear benefits to using AI in the agriculture sector. However, without effective governance, there are risks and challenges that can limit the benefits that can be obtained from using AI. For instance, ineffective policies that fail to outline liability in the event of adverse outcomes, such as crop loss resulting from ineffective decision-making by an AI technology or tool. We present a definition of AI governance in agriculture, in addition to a novel taxonomy which is comprised of nine principles: inclusivity & education, trustworthiness, custodianship & liability, law, sustainable & ethical development, transparency, privacy & security, bias, and data quality. Following a scoping review methodology, we conduct an analysis of the existing literature on AI governance in agriculture through the lens of the nine principles. Additionally, we uncover factors which support the principles of AI governance outlined in the discussion. Furthermore, identifying research gaps provides a clear road map for future directions, which can support researchers, policymakers, and practitioners. This study supports the ongoing development of AI governance and sheds light on the critical need for tailored governance for the agriculture sector.