# Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards

arXiv: https://arxiv.org/abs/1911.12889 · HTML: https://mlsanity.com/paper/1911.12889/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards](https://mlsanity.com/paper/2112.04363/) (2021)
- [Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards](https://mlsanity.com/paper/2208.02483/) (2022)
- [Tomato Maturity Recognition with Convolutional Transformers](https://mlsanity.com/paper/2307.01530/) (2023)
- [Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything](https://mlsanity.com/paper/2412.03472/) (2024)
- [Deep Learning approach for Classifying Trusses and Runners of Strawberries](https://mlsanity.com/paper/2207.02721/) (2022)
- [Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Clouds](https://mlsanity.com/paper/2411.07799/) (2024)
- [Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting](https://mlsanity.com/paper/2105.03832/) (2021)
- [HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees](https://mlsanity.com/paper/2208.00002/) (2022)
- [3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud](https://mlsanity.com/paper/2311.04699/) (2023)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)
- [Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications](https://mlsanity.com/paper/2508.00900/) (2025)

## Abstract (arXiv)

Robotic harvesting of fruits in orchards is a challenging task, since high density and overlapping of fruits and branches can heavily impact the success rate of robotic harvesting. Therefore, the vision system is demanded to provide comprehensive information of the working environment to guide the manipulator and gripping system to successful detach the target fruits. In this study, a deep learning based one-stage detector DaSNet-V2 is developed to perform the multi-task vision sensing in the working environment of apple orchards. DaSNet-V2 combines the detection and instance segmentation of fruits and semantic segmentation of branch into a single network architecture. Meanwhile, a light-weight backbone network LW-net is utilised in the DaSNet-V2 model to improve the computational efficiency of the model. In the experiment, DaSNet-V2 is tested and evaluated on the RGB-D images of the orchard. From the experiment results, DaSNet-V2 with lightweight backbone achieves 0.844, 0.858, and 0.795 on the F 1 score of the detection, and mean intersection of union on the instance segmentation of fruits and semantic segmentation of branches, respectively. To provide a direct-viewing of the working environment in orchards, the obtained sensing results are illustrated by 3D visualisation . The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.