<p>The integration of Artificial Intelligence (AI) into agriculture has transformed plant health monitoring, yet challenges in interpretability and transparency hinder its broader adoption. Explainable Artificial Intelligence (XAI) addresses these limitations by providing clarity into AI-driven decision-making processes, fostering trust and understanding among stakeholders. Although several review papers explore XAI in general, there is limited focus on its specific applications in plant health monitoring. Moreover, existing surveys in plant health monitoring primarily focus on the technical development of models, often neglecting the critical aspect of explainability. The proposed survey bridges this gap by investigating diverse methodologies along with XAI techniques used specifically for disease detection, pest identification, and overall plant health assessment, offering a targeted perspective on this critical domain. This study also reviews some publicly available datasets, offering valuable insights into data resources supporting agricultural research. Additionally, the study discusses key challenges associated with implementing XAI in agriculture, including dataset limitations, model robustness and XAI integration with existing agricultural systems. Furthermore, the study also proposes future research directions, including integration of XAI in early model development, robustness of XAI against adversarial attacks, real time monitoring, development of benchmark datasets and so on. The purpose of this survey is to serve as a valuable asset for researchers, practitioners and policymakers aiming to grasp the present-day scenario of XAI in plant health monitoring and chart a course for future advancements in the field.</p>