Ai-Driven Video Analytics in IoT for Healthcare and Agricultural Monitoring Applications

Abstract

This paper explores the integration of AI-driven video analytics with Internet of Things (IoT) platforms for two high-impact domains: healthcare (patient monitoring, fall detection, ward safety) and agriculture (crop / livestock monitoring, pest/disease detection). We review recent advances in edge AI, lightweight deep learning models, and hybrid edge–cloud architectures; describe typical system architectures and processing pipelines; present representative use cases and short case summaries; and discuss technical, ethical, and deployment challenges. The paper closes with practical recommendations and future research directions to improve robustness, privacy, and scalability.

KEYWORDS: AI-driven video analytics, Internet of Things (IoT), edge AI, healthcare monitoring, precision agriculture, privacy-preserving inference, federated learning.