Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering

2026-09-09

AI Summary

本文通过结合视觉变换器特征嵌入、基于模糊聚类的任务构建和基于梯度的元学习，提出了一种少样本回归框架以解决植物生长估计中标签数据稀缺的问题。

Abstract

Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regression framework that combines Vision Transformer (ViT) feature embeddings, clustering-based task construction, and gradient-based meta-learning, and show that task construction in embedding space is a primary driver of performance. The approach leverages an unlabeled image pool to organize data into structured tasks using fuzzy c-means clustering, enabling efficient learning from a small number of labeled samples. We systematically evaluate meta-learning methods and show that second-order methods (e.g., Model-Agnostic Meta-Learning variants such as MAML++) outperform classical baselines in the few-shot regime. Furthermore, intra-cluster support selection has a limited and dataset-dependent impact. Experiments on two plant datasets show that structured task design combined with meta-learning enables reliable plant growth estimation under severe label scarcity.

Problem

• plant growth estimation
• greenhouse monitoring
• labeled data
• few-shot regression
• Vision Transformer

Innovation

• few-shot regression
• Vision Transformer
• fuzzy c-means clustering
• meta-learning
• MAML++