A Survey on AI-Based Multilingual Robotic Assistant for Smart Agriculture

Intelligent robotic assistants increasingly rely on advances in speech, vision, and navigation technologies. Multilingual voice interaction, powered by Automatic Speech Recognition (ASR) and Natural Language Processing (NLP), enables seamless human–robot communication across languages. Real-time vision, supported by Convolutional Neural Networks (CNNs) and detection models such as YOLOv5, enhances object recognition, tracking, and scene understanding. At the same time, autonomous navigation methods, path planning, and obstacle avoidance ensure safe mobility. This survey reviews improvements across these domains, outlines key challenges such as adaptability and robustness, and highlights opportunities for advancing human-centered robotic assistants