Few-Shot Fruit Segmentation via Transfer Learning

TL;DR

This paper introduces a transfer learning-based few-shot segmentation framework for infield fruit detection, enabling accurate segmentation with minimal labeled data, which is crucial for agricultural automation tasks.

Contribution

The work presents a novel transfer learning approach for few-shot fruit segmentation, addressing data scarcity in agricultural domains and distinguishing between different fruit states.

Findings

Pre-trained models effectively segment fruits with few labeled images.

Models can differentiate between on-tree and fallen fruits.

Transfer learning improves segmentation accuracy in limited data scenarios.

Abstract

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.