aiqbench analysis
This research demonstrates the integration of large language models and speech recognition (Whisper) to enable voice-controlled UAVs for autonomous fruit harvesting, addressing labor shortages in tropical agriculture. The system combines computer vision for ripeness detection with adaptive path planning, validated through Gazebo simulations rather than physical deployment. While the application is agricultural, the core technical contribution lies in advancing multimodal AI control systems for robotics.
IVG-UAV: An Intelligent Voice-Guided UAV System for Autonomous Ripe Fruit Harvesting with Vision-Based Classification and Adaptive Path Planning
arXiv:2610.09398v1 Announce Type: new Abstract: In tropical regions, their agricultural sectors remain highly dependent on manual labor for fruit harvesting. On large-scale farms, this dependency often results in significant labor cost and logistic complexities. This project presents the development and simulation of a voice-controlled Unmanned Aerial Vehicle (UAV) system designed to automate harvesting tasks in extensive plantations. The proposed system integrates speech recognition using Whisper [1] and LLM, computer vision-based ripeness classification, and adaptive path planning within a unified framework. The entire system is modeled and validated in a Gazebo simulation environment, allowing performance evaluation under controlled agricultural scenarios
Abstract:In tropical regions, their agricultural sectors remain highly dependent on manual labor for fruit harvesting. On large-scale farms, this dependency often results in significant labor cost and logistic complexities. This project presents the development and simulation of a voice-controlled Unmanned Aerial Vehicle (UAV) system designed to automate harvesting tasks in extensive plantations. The proposed system integrates speech recognition using Whisper [1] and LLM, computer vision-based ripeness classification, and adaptive path planning within a unified framework. The entire system is modeled and validated in a Gazebo simulation environment, allowing performance evaluation under controlled agricultural scenarios
Robotics (cs.RO)
arXiv:2610.09398 [cs.RO]
(or arXiv:2610.09398v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.09398 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 187 words
