The increasing demand for bearable farming and increased food production has helped drive the adoption of digital technologies in farming. Cloud computing is one such technology that plays a critical role by providing scalable, real-time, and data-centric solutions. This chapter reviews AgriCloud, a cloud-based platform intended for scalable crop monitoring and smart decision support. It supports smooth integration of IoT-based data acquisition, cloud storage and computation, and machine learning-based analytics to facilitate precision agriculture. AgriCloud platforms enable farmers and stakeholders to remotely track crop health, anticipate environmental and biological stressors, and make decisions based on precise, real-time data. This chapter describes the architectural building blocks of such systems, including sensor networks, cloud platforms, data analytics engines, and user interfaces. It also converses prime use cases like irrigation scheduling, disease and pest forecasting, yield prediction, and resource allocation. Comparative analysis by the chapter delineates several existing AgriCloud models and platforms and evaluates their strengths, weaknesses, and scalability across various agricultural contexts. In addition, it traces the principal challenges including data interoperability, rural-area infrastructure constraints, cybersecurity, and the digital divide in technology espousal among farmers. The conclusion of the chapter presents future opportunities for research, involving the incorporation of edge computing, 5G networks, AI developments, and climate-resilient decision-making. Generally, this chapter presents an in-depth analysis of how cloud-based platforms such as AgriCloud have the potential to transform farming by promoting smarter, more efficient, and sustainable agriculture.