Greenhouse workers face unique occupational hazards including extreme thermal conditions, high humidity, chemical exposure, and physically demanding tasks that collectively induce acute and chronic physiological stress, yet traditional health monitoring approaches lack the temporal resolution and objectivity required for effective intervention. Internet of Things (IoT) technologies integrated with wearable biosensors enable continuous, real-time monitoring of worker physiological status and environmental conditions, facilitating early detection of heat stress, cardiovascular strain, and fatigue before adverse health outcomes occur. This article examines IoT-based stress detection architectures for greenhouse environments, focusing on sensor technologies measuring heart rate variability, galvanic skin response, core body temperature, and environmental parameters including ambient temperature, humidity, and atmospheric composition. Machine learning algorithms process multi-modal sensor streams to classify stress levels, predict heat-related illness risk, and generate automated alerts to workers and supervisors. Case implementations demonstrate 35-50% reduction in heat stress incidents and improved worker productivity through timely interventions. Key challenges include sensor accuracy under harsh greenhouse conditions, data privacy concerns, system integration complexity, and economic barriers to adoption among small-scale operations. Future developments in edge computing, artificial intelligence, and digital twin technologies promise enhanced predictive capabilities and seamless integration with smart greenhouse management systems, advancing both worker safety and agricultural productivity in protected cultivation environments.