Crop diseases are incessant and significant challenges to global food security. Conventional disease control means are still utilized as the primary mitigation model, but they fall short at providing quick and precision-based responses that are required for quick outbreak containment, resulting in significant yield losses. New advances in Artificial Intelligence of Things (AIoT) technologies currently enabled novel capabilities to predict activities and initiate early-stage interventions in disease progression. This longitudinal scoping review, organized according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol, examined 100 peer-reviewed articles from IEEE Xplore, PubMed, Springer, Elsevier, and MDPI to investigate AIoT applications in the prediction and control of crop diseases and assess the quality of articles published between 2019 and 2024. The literature reviewed indicated a variety of predictive models in farming that fused AI and IoT. Notably, the deployment of federated learning was suggested as a solution to minimize the risk of privacy breaches by training models using locally stored data whose heterogeneity allows them to avoid sharin
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