ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Field Robotics
This article is part of the Research TopicAI and Robotics for Smart AgricultureView all 6 articles
Osiris++ : Hierarchical Representations for Robotic-Enabled Precision Agriculture
Provisionally accepted
- University of Cape Town, Cape Town, South Africa
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There has been significant development in agricultural robotics over the past few years in the pursuit of optimizing efficiency and addressing issues such as labour shortages and humans performing hazardous and arduous tasks. Despite this, human-robot interaction in the agricultural sector remains largely unchanged, often requiring technical expertise which hinders wide-scale adoption. This problem is particularly pronounced in the African context, where limited technical exposure and linguistic diversity pose significant barriers to the adoption of these technologies. Whilst there has been development of alternative means for human-robot collaboration, these methods are currently limited to indoor structured environments. In this work, we introduce Osiris++, a flexible approach designed to allow for seamless communication between robots and humans on an array of precision agriculture tasks. We validate and evaluate the performance of Osiris++ in real-world agricultural environments by demonstrating that the system is able to create accurate and useful scene-graphs that aid in solving assigned tasks. This paves the way for the possibility of allowing natural language instructions, including in African languages, to be issued to robots within the agricultural sector
Keywords: field robotics, human-robot interaction, Perception, precision agriculture, Robotics
Received: 25 Oct 2025; Accepted: 20 Jan 2026.
Copyright: © 2026 Amayo, Mukuddem, Speed-Andrews, Maweni, Nanyaro, Sojen and Hsiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Paul Amayo
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