AGRO: An Autonomous AI Rover for Precision Agriculture

TL;DR

This paper presents AGRO, an autonomous ground rover that uses machine learning, computer vision, and sensors to assist farmers by autonomously navigating fields, collecting data, and supporting data-driven agricultural decisions.

Contribution

The development of AGRO, a novel autonomous ground rover that integrates multiple technologies for precision agriculture and real-time environmental mapping.

Findings

AGRO can accurately determine pistachio yields.

It performs self-localization and obstacle avoidance.

Supports real-time environmental mapping.

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.