Autonomous Strawberry Picking Robotic System (Robofruit)
Soran Parsa, Bappaditya Debnath, Muhammad Arshad Khan, Amir Ghalamzan, E.

TL;DR
This paper presents a novel, reconfigurable robotic system for selective strawberry harvesting capable of handling dense clusters and occlusions, with high accuracy in detection and successful harvesting rates demonstrated in real fields.
Contribution
The work introduces a modular robotic system with a specialized picking head and a new perception system, including two datasets, to improve strawberry harvesting in complex scenarios.
Findings
Perception system detects strawberries with 95% accuracy.
System harvests 87% of detected strawberries.
Harvesting success rate is 83% for pluckable fruits.
Abstract
Challenges in strawberry picking made selective harvesting robotic technology demanding. However, selective harvesting of strawberries is complicated forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, e.g., picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g. high-yielding and/or disease-resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. The features allow the system to deal with very complex picking scenarios, e.g. dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a picking head with 2.5…
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Taxonomy
TopicsSmart Agriculture and AI · Plant Virus Research Studies · Plant Disease Management Techniques
