  Agriculture industries often face challenges in manual tasks such as
planting, harvesting, fertilizing, and detection, which can be time consuming
and prone to errors. The "Agricultural Robotic System" project addresses these
issues through a modular design that integrates advanced visual, speech
recognition, and robotic technologies. This system is comprised of separate but
interconnected modules for vision detection and speech recognition, creating a
flexible and adaptable solution. The vision detection module uses computer
vision techniques, trained on YOLOv5 and deployed on the Jetson Nano in
TensorRT format, to accurately detect and identify different items. A robotic
arm module then precisely controls the picking up of seedlings or seeds, and
arranges them in specific locations. The speech recognition module enhances
intelligent human robot interaction, allowing for efficient and intuitive
control of the system. This modular approach improves the efficiency and
accuracy of agricultural tasks, demonstrating the potential of robotics in the
agricultural industry.
