Rationale, Aims and ObjectivesModern smart farming systems increasingly rely on Artificial Intelligence (AI) to automate monitoring, climate control and crop management. However, agricultural AI systems often face two major challenges: limited high-quality farming data for diverse scenarios and lack of transparency in AI-driven decisions. This research aimed to develop an explainable AI framework capable of generating realistic synthetic farming data while also improving trust in automated decision-support systems. The study introduced a novel Conditional Time-Transformer model that generates context-aware greenhouse data using environmental conditions such as weather, time patterns and operational settings, while integrating explainable AI methods to assess how AI systems make decisions. Research FindingsThe proposed AI framework improved the quality and realism of synthetic greenhouse data compared to existing generative AI approaches. The model achieved stronger alignment with real-world greenhouse conditions and improved downstream decision-making performance for greenhouse control tasks. Importantly, the research demonstrated that explainable AI techniques can help evaluate whether AI systems trained on synthetic data make decisions in ways that remain consistent with real farming behaviour. The findings showed that combining conditional generative AI with explainability can support more transparent, reliable and trustworthy smart farming systems while reducing dependence on expensive long-term agricultural data collection. Policy ImplicationsThe findings highlight the need for governance frameworks that promote transparency and accountability in AI-driven agricultural technologies. Policymakers should encourage the use of explainable AI validation methods before deploying AI systems in high-impact farming operations. The research also supports public investment in trustworthy AI infrastructure for agriculture, particularly for climate-smart and resource-efficient farming systems. Synthetic data generation may help overcome agricultural data shortages while preserving operational privacy and reducing deployment costs. However, regulatory guidelines should require ongoing validation of AI reasoning and decision consistency to ensure safe and reliable adoption within real-world agricultural environments. Industry RecommendationsAgricultural technology providers and greenhouse operators should integrate explainable AI tools into smart farming platforms to improve transparency and farmer trust. Companies deploying AI-based greenhouse automation should validate AI decisions using both performance metrics and explainability assessments before operational use. The use of synthetic data generation can help organisations train AI systems under rare or extreme environmental conditions without waiting for long-term field data collection. Industry stakeholders should also prioritise human-in-the-loop monitoring for AI-assisted climate-control decisions, particularly in safety-critical situations where incorrect predictions could affect crop productivity, resource usage or operational stability.