Voice-Controlled Autonomous Agri-Robot for Organic Farming Pest and Disease Monitoring

Abstract

Safeguarding agricultural crops, particularly against threats such as pink bollworm infestations, is paramount for sustaining agricultural productivity. Our research paper proposes a novel solution for autonomous crop monitoring and protection by integrating the Robot Operating System (ROS) and a robust detection model. The study introduces an autonomous robot capable of identifying pink bollworm infestations and tracking diseases impacting cotton yields. Leveraging drones for field assessment, the robot's path planning is intricately linked to this process. Additionally, a customized dataset is created to enhance the robot's detection abilities, using which a YOLOV8 model is trained, exhibiting a performance metrics: mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. Furthermore, the paper proposes integrating voice command control for the robot to address challenges posed by difficult terrains. This feature allows for guided navigation and recovery using voice commands when encountering obstacles or challenging environments. By incorporating voice control, the system minimizes the need for human intervention in robot recovery, enhancing operational efficiency and reducing labour requirements. The integrated system represents a digital solution tailored for organic farming, offering flexibility beyond cotton crops to address diverse agricultural challenges. This research presents a novel and comprehensive approach to autonomous crop monitoring and protection, addressing specific challenges posed by pink bollworm infestations and crop diseases in cotton cultivation.