# Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting

arXiv: https://arxiv.org/abs/2003.13298 · HTML: https://mlsanity.com/paper/2003.13298/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

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

- [O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments](https://mlsanity.com/paper/2303.04884/) (2023)
- [CitDet: A Benchmark Dataset for Citrus Fruit Detection](https://mlsanity.com/paper/2309.05645/) (2023)
- [Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environment](https://mlsanity.com/paper/2410.19869/) (2024)
- [A Real-time Low-cost Artificial Intelligence System for Autonomous Spraying in Palm Plantations](https://mlsanity.com/paper/2103.04132/) (2021)
- [Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting](https://mlsanity.com/paper/2105.03832/) (2021)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)

## Abstract (arXiv)

In this research, a fully neural network based visual perception framework for autonomous apple harvesting is proposed. The proposed framework includes a multi-function neural network for fruit recognition and a Pointnet grasp estimation to determine the proper grasp pose to guide the robotic execution. Fruit recognition takes raw input of RGB images from the RGB-D camera to perform fruit detection and instance segmentation, and Pointnet grasp estimation take point cloud of each fruit as input and output the prediction of grasp pose for each of fruits. The proposed framework is validated by using RGB-D images collected from laboratory and orchard environments, a robotic grasping test in a controlled environment is also included in the experiments. Experimental shows that the proposed framework can accurately localise and estimate the grasp pose for robotic grasping.