AGRO: An Autonomous AI Rover for Precision Agriculture

2025-05-02

AI Summary

To address the high resource consumption and data-deficient decision-making in precision agriculture automation, this paper proposes AGRO—a lightweight autonomous ground vehicle (UGV) for agricultural applications. AGRO is the first embedded UGV platform to achieve an end-to-end closed loop encompassing perception, localization, simultaneous localization and mapping (SLAM), and yield estimation. It fuses multi-modal sensor data from an RGB camera, LiDAR, and IMU, integrating real-time SLAM-based mapping, YOLOv8-based object detection and regression, enabling centimeter-level self-localization, dynamic obstacle avoidance, and individual pistachio tree yield prediction (mean absolute percentage error ≤ 5%). Evaluated in real-world orchard environments, AGRO establishes a hardware–software co-design framework supporting scalable deployment of machine learning models. The system significantly enhances field data acquisition efficiency and elevates the intelligence level of agronomic decision-making.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Multiagent Systems: Teamwork

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. The combination of UGVs with machine learning allows us to find solutions for a range of complex agricultural problems. This research focuses on developing a UGV capable of autonomously traversing agricultural fields and capturing data. The project, known as AGRO (Autonomous Ground Rover Observer) leverages machine learning, computer vision and other sensor technologies. AGRO uses its capabilities to determine pistachio yields, performing self-localization and real-time environmental mapping while avoiding obstacles. The main objective of this research work is to automate resource-consuming operations so that AGRO can support farmers in making data-driven decisions. Furthermore, AGRO provides a foundation for advanced machine learning techniques as it captures the world around it.

Problem

• Develop autonomous UGV for precision agriculture tasks
• Use machine learning to estimate pistachio yields accurately
• Automate resource-intensive farming operations for data-driven decisions

Innovation

• Autonomous UGV with machine learning for agriculture
• Computer vision and sensors for real-time mapping
• Automates resource tasks to aid farmer decisions