The integration of remote sensing and machine learning in agriculture is transforming the industry by providing insights and predictions through data analysis. This combination leads to improved yield prediction and water management, resulting in increased efficiency, better yields, and more sustainable agricultural practices. Achieving the United Nations' Sustainable Development Goals, especially "zero hunger," requires the investigation of crop yield and precipitation gaps, which can be accomplished through, the usage of artificial intelligence (AI), machine learning (ML), remote sensing (RS), and the internet of things (IoT). By integrating these technologies, a robust agricultural mobile or web application can be developed, providing farmers and decision-makers with valuable information and tools for improving crop management and increasing efficiency. Several studies have investigated these new technologies and their potential for diverse tasks such as crop monitoring, yield prediction, irrigation management, etc. Through a critical review, this paper reviews relevant articles that have used RS, ML, cloud computing, and IoT in crop yield prediction. It reviews the current state-of-the-art in this field by critically evaluating different machine-learning approaches proposed in the literature for crop yield prediction and water management. It provides insights into how these methods can improve decision-making in agricultural production systems. This work will serve as a compendium for those interested in yield prediction in terms of primary literature but, most importantly, what approaches can be used for real-time and robust prediction.

翻译：遥感与机器学习在农业中的整合正通过数据分析提供洞察与预测，推动行业变革。这种结合带来了产量预测与水资源管理的改进，从而提升了效率、提高了产量，并促进更可持续的农业实践。实现联合国可持续发展目标（尤其是"零饥饿"目标）需要研究作物产量与降水差距，这可通过人工智能（AI）、机器学习（ML）、遥感（RS）和物联网（IoT）的应用实现。通过整合这些技术，可开发出健壮的农业移动端或网页端应用程序，为农民和决策者提供改善作物管理、提升效率的宝贵信息与工具。多项研究已探索这些新技术在作物监测、产量预测、灌溉管理等多样化任务中的潜力。本文通过批判性综述，回顾了在作物产量预测中应用遥感、机器学习、云计算和物联网的相关文献。通过批判性评估文献中提出的用于作物产量预测和水资源管理的不同机器学习方法，本文评述了该领域当前的最新技术水平，并阐述了这些方法如何改善农业生产系统中的决策制定。本研究将为关注产量预测的学者提供主要文献汇编，但更重要的是，揭示了可用于实时与稳健预测的方法路径。