# Few-Shot Fruit Segmentation via Transfer Learning

arXiv: https://arxiv.org/abs/2405.02556 · HTML: https://mlsanity.com/paper/2405.02556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images](https://mlsanity.com/paper/1903.08469/) (2019)
- [DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1904.02216/) (2019)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [Real-time Semantic Segmentation with Fast Attention](https://mlsanity.com/paper/2007.03815/) (2020)
- [DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/2003.11883/) (2020)
- [Global Aggregation then Local Distribution in Fully Convolutional Networks](https://mlsanity.com/paper/1909.07229/) (2019)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)
- [Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion](https://mlsanity.com/paper/1804.00307/) (2018)
- [ShuffleSeg: Real-time Semantic Segmentation Network](https://mlsanity.com/paper/1803.03816/) (2018)
- [CitDet: A Benchmark Dataset for Citrus Fruit Detection](https://mlsanity.com/paper/2309.05645/) (2023)

## Built on by (most cited first, 1 of 1)

- [CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting](https://mlsanity.com/paper/2601.00207/) (2026)

## Abstract (arXiv)

Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.