# Review of Fruit Tree Image Segmentation

arXiv: https://arxiv.org/abs/2412.14631 · HTML: https://mlsanity.com/paper/2412.14631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 227 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [A survey of the Vision Transformers and their CNN-Transformer based Variants](https://mlsanity.com/paper/2305.09880/) (2023)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments](https://mlsanity.com/paper/2312.07935/) (2023)
- [A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards](https://mlsanity.com/paper/1810.09499/) (2018)
- [Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards](https://mlsanity.com/paper/1911.12889/) (2019)
- [Multispecies fruit flower detection using a refined semantic segmentation network](https://mlsanity.com/paper/1809.10080/) (2018)
- [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://mlsanity.com/paper/2303.02632/) (2023)
- [Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting](https://mlsanity.com/paper/2003.13298/) (2020)
- [A Survey of Robotic Harvesting Systems and Enabling Technologies](https://mlsanity.com/paper/2207.10457/) (2022)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning](https://mlsanity.com/paper/2010.06879/) (2020)
- [Field Evaluations of A Deep Learning-based Intelligent Spraying Robot with Flow Control for Pear Orchards](https://mlsanity.com/paper/2102.07313/) (2021)
- [SimTreeLS: Simulating aerial and terrestrial laser scans of trees](https://mlsanity.com/paper/2011.11954/) (2020)
- [Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows](https://mlsanity.com/paper/1809.00075/) (2018)
- [A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees](https://mlsanity.com/paper/2102.03700/) (2021)
- [In-field high throughput grapevine phenotyping with a consumer-grade depth camera](https://mlsanity.com/paper/2104.06945/) (2021)
- [Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species](https://mlsanity.com/paper/2209.04618/) (2022)
- [Automatic segmentation of trees in dynamic outdoor environments](https://mlsanity.com/paper/1702.07611/) (2017)
- [Graph-based methods for analyzing orchard tree structure using noisy point cloud data](https://mlsanity.com/paper/2009.13727/) (2020)
- [Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds](https://mlsanity.com/paper/2012.13721/) (2020)
- [A vision-based robotic system for precision pollination of apples](https://mlsanity.com/paper/2409.19918/) (2024)

## Abstract (arXiv)

Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 158 relevant papers collected using a newly designed crawling review method. These papers are systematically reviewed based on a taxonomy that sequentially considers the method, image, task, and fruit. This taxonomy will assist readers to intuitively grasp the big picture of these research activities. Our review reveals that the most noticeable deficiency of the previous studies was the lack of a versatile dataset and segmentation model that could be applied to a variety of tasks and environments. Six important future research tasks are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module.