Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders.

由并发数据和先进技术驱动的植物病害预测模型是准确预测病害爆发以实现可持续和高产农业系统的可靠工具。物联网（IoT）、机器学习（ML）技术和人工智能（AI）的优化集成，进一步增强了这些模型的能力，为农民提供主动的疾病控制措施，以实现现代农业，表现为高效的资源管理、减少疾病和预防疾病。更高的农作物产量。本文总结了疾病预测模型在作物管理中的作用，通过 (a) 模型验证测试的技术基础和需求，(b) 强调了人工智能和机器学习在疾病预测中的进展和应用、该领域的挑战和未来方向）疾病预测的进步以及高质量公开数据的重要性；以及（c）开发透明和可解释的开源人工智能模型的挑战和未来方向。这些模型的进一步改进需要投资于持续的创新研究以及农业利益相关者之间的协作和数据共享。