# Autonomous Strawberry Picking Robotic System (Robofruit)

arXiv: https://arxiv.org/abs/2301.03947 · HTML: https://mlsanity.com/paper/2301.03947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 73 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Agricultural Robotics: The Future of Robotic Agriculture](https://mlsanity.com/paper/1806.06762/) (2018)
- [Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion](https://mlsanity.com/paper/1804.00307/) (2018)
- [Action Conditioned Tactile Prediction: case study on slip prediction](https://mlsanity.com/paper/2205.09430/) (2022)
- [Autonomous Sweet Pepper Harvesting for Protected Cropping Systems](https://mlsanity.com/paper/1706.02023/) (2017)
- [dPMP-Deep Probabilistic Motion Planning: A use case in Strawberry Picking Robot](https://mlsanity.com/paper/2208.09074/) (2022)
- [Proactive slip control by learned slip model and trajectory adaptation](https://mlsanity.com/paper/2209.06019/) (2022)
- [Deep Movement Primitives: toward Breast Cancer Examination Robot](https://mlsanity.com/paper/2202.09265/) (2022)
- [Peduncle Gripping and Cutting Force for Strawberry Harvesting Robotic End-effector Design](https://mlsanity.com/paper/2207.12552/) (2022)
- [Design and Evaluation of a Novel Cable-Driven Gripper with Perception Capabilities for Strawberry Picking Robots](https://mlsanity.com/paper/1804.09771/) (2018)

## Built on by (most cited first, 1 of 1)

- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)

## Abstract (arXiv)

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 DOF (2 independent mechanisms and 1 dependent cutting system) capable of removing possible occlusions and harvesting targeted strawberries without contacting fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localise strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new datasets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.