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

Abstract

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.

Introduction

The convergence of AI, robotics, and computer vision is revolutionizing precision agriculture, especially as food demand grows and agricultural labor declines. This survey examines AI-driven robotic systems that enhance farming through:

• Multilingual voice interaction

• Vision-based object detection

• Autonomous field navigation

Key Objectives

To address critical agricultural challenges (labor shortages, inefficiency, and communication barriers), future robotic assistants must:

1. Understand voice commands in local languages

2. Detect and classify crops, weeds, pests, etc., in real time

3. Navigate autonomously in unstructured terrains

4. Operate without traditional interfaces (hands-free)

Technological Components

1. Multilingual Voice Interfaces

• Enabled by ASR, NLP, and tools like Google Speech API

• Make robotic systems accessible to non-English-speaking farmers

• Challenges: Performance drops in noisy, low-resource language settings

2. Vision-Based Object Detection

• Uses CNNs, especially YOLOv5, for identifying crops, pests, weeds, and tools

• High accuracy in lab tests but less effective in field conditions

• Needs lightweight models for real-time use on edge devices

3. Autonomous Navigation

• Uses ArUco markers, GPS, and SLAM techniques

• Allows mobility in structured environments but struggles in dynamic, uneven terrains

• Needs hybrid approaches for robust navigation

Literature Survey Highlights

Discussion & Insights

A. Voice Interaction

• Vital for inclusivity in linguistically diverse farming communities

• Requires better datasets and noise-resistant, domain-specific models

B. Computer Vision

• YOLOv5 and similar models are effective but need field-specific optimization

• Must handle varied lighting, occlusion, and real-world clutter

C. Autonomous Navigation

• ArUco markers and GPS offer a starting point

• Real-world success requires adaptive, sensor-fusion-based navigation

D. System Integration

• Major gap: few systems combine voice, vision, and navigation

• Modular, open-source platforms are key for scalable, localized deployment

E. Broader Applications

• Beyond agriculture: healthcare, home automation, customer service

• Requires interdisciplinary collaboration and ethical AI practices

Technological Spotlight

• YOLOv5: Real-time, grid-based object detection using CNNs; optimized for edge deployment (e.g., Raspberry Pi)

• Large Language Models (LLMs): Transformer-based deep learning systems capable of understanding and generating human language; useful for interactive farmer support

Future Research Directions

1. Adaptive Learning for crop-specific tasks

2. Robotic Soil Sampling for real-time health monitoring

3. Sustainable Power via solar or other renewables

4. Personalized Interaction tailored to individual farmer needs

Conclusion

The study identifies a strong movement toward designing robotic assistants that are more adaptive, inclusive, and context-sensitive. Nevertheless, unresolved challenges persist, including limited progress in handling low-resource languages, maintaining reliable visual recognition under unpredictable conditions, and enabling robust navigation in unstructured areas. Future directions should aim at advancing cross-lingual natural language processing models, designing efficient yet precise object detection architectures, and developing navigation strategies validated in real-world scenarios. Bridging these gaps can result in robotic assistants that are more intelligent, user-friendly, and impactful across varied applications, ultimately promoting human-centered automation on a broader scale.

References

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Copyright

Copyright © 2025 Lavanya D R, Dr. Kamalakshi Naganna, Sahana N O, Manasa H B, Anushree H S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.