# A Survey of Robotic Harvesting Systems and Enabling Technologies

arXiv: https://arxiv.org/abs/2207.10457 · HTML: https://mlsanity.com/paper/2207.10457/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 6 of 6)

- [Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation](https://mlsanity.com/paper/2411.18335/) (2024)
- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)
- [Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation](https://mlsanity.com/paper/2409.09921/) (2024)
- [Synthetic Data Generation for Anomaly Detection on Table Grapes](https://mlsanity.com/paper/2412.12949/) (2024)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)
- [Robust Flower Cluster Matching Using The Unscented Transform](https://mlsanity.com/paper/2503.20631/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.