# Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

arXiv: https://arxiv.org/abs/1706.02023 · HTML: https://mlsanity.com/paper/1706.02023/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots](https://mlsanity.com/paper/2303.08923/) (2023)
- [Explicitly incorporating spatial information to recurrent networks for agriculture](https://mlsanity.com/paper/2206.13406/) (2022)
- [Task Success Prediction for Open-Vocabulary Manipulation Based on Multi-Level Aligned Representations](https://mlsanity.com/paper/2410.00436/) (2024)
- [Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach](https://mlsanity.com/paper/2505.03702/) (2025)
- [Autonomous Strawberry Picking Robotic System (Robofruit)](https://mlsanity.com/paper/2301.03947/) (2023)
- [Shape Back-Projection In 3D Scenes](https://mlsanity.com/paper/2101.06409/) (2021)
- [ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations](https://mlsanity.com/paper/2609.10918/) (2026)

## Abstract (arXiv)

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance.