Cloud computing has become a crucial part of smart farming systems. It offers various services, from data storage to data analytics and visualization. However, selecting a feasible platform is challenging since many factors and criteria need to be considered by decision-makers based on the organization's requirements to select the most optimal cloud solution. This study aimed to provide a systematic approach to selecting cloud computing-based data analytics platforms for precision farming. There are three important stages within the proposed approach: the preparation stage, the integrated model using Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an evaluation framework, and model evaluation. Three cloud computing platforms were evaluated using the proposed model for a novel smart farming project: Amazon Web Services, Google Cloud Platform, and Microsoft Azure. The results show that Google Cloud Platform (S2) is best optimal platform for the smart farming project called smart-in-ag based on the criteria and requirements defined by stakeholders. To validate the consistency and robustness of our proposed model, the sensitivity analysis method was applied to 13 cases. It was demonstrated that the proposed approach is consistent and robust for helping the experts who choose a cloud computing-based data analytics platform in a smart farming project. To the best of our knowledge, this study is the first application for selection the cloud computing platform for a real smart farming project.