Authors: Khade Pranjal Dilip, Dr.Anand. A. Khatri, Professor Bangar Abhishek, Professor S.B.Bhosale
Abstract: Farm workers operating in sugarcane fields, forests, tea plantations, and other areas with thick vegetation may encounter hazardous animals and insects that are difficult to notice before approaching them. Conventional precautions, including sticks, torches, protective footwear, and visual observation, offer limited capability for identifying concealed threats in advance. Delays in communication and access to medical assistance can further increase the risks associated with such incidents. Recent progress in Artificial Intelligence (AI), computer vision, thermal imaging, edge computing, Global Positioning System (GPS) technology, and the Internet of Things (IoT) provides opportunities to develop wearable sys-tems for improved agricultural safety. This paper presents a focused narrative review of research relevant to an AI-enabled safety cap, covering animal recognition, snake detection, thermal-based wildlife monitoring, edge deployment, and IoT- supported alert systems. The reviewed approaches are discussed across three areas: sensing methods, including visible-light cameras, thermal sensors, and supplementary positioning or motion sensors; deep-learning techniques, including YOLO -based detectors, transformer architectures, lightweight models, and object tracking; and system de-signs combining local processing, offline data handling, and cloud connectivity. Published results indicate that some animal and snake detection models achieve approximately 92–98% on selected evaluation metrics, while one thermal wildlife detection approach reports 89.1% mAP50 with approximately 1.45 million parameters. Certain lightweight YOLOv8 models have also demonstrated processing rates of around 52–65 frames per second on Jetson -class hard-ware, although performance may vary across devices and experimental settings. Despite these advances, much of the existing research relies on fixed cameras, aerial platforms, or controlled datasets. Limitations include restricted species coverage, insufficiently documented datasets partitioning, and limited evaluation under real agricultural conditions. The reviewed evidence does not establish the field performance of an integrated, multi-camera, head-worn safety device incorporating behaviour analysis, battery endurance assessment, and emergency alert testing. To address these gaps, this paper outlines a conceptual worker-to-cloud architecture combining visual and thermal sensing, edge-based inference, multi modal warnings, optional GPS-enabled SOS communication, and systematic field evaluation. The review also identifies research priorities for developing a practical and reliable wearable safety solution for agricultural workers.
