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

A few-shot regression framework combining Vision Transformer (ViT) feature embeddings, fuzzy c-means clustering-based task construction, and gradient-based meta-learning enables reliable plant growth estimation under severe label scarcity, according to an arXiv paper (arXiv:2609.10749v1). The researchers report that second-order meta-learning methods such as MAML++ outperform classical baselines in the few-shot regime, and that task construction in embedding space is the primary driver of performance. Experiments on two plant datasets show intra-cluster support selection has a limited and dataset-dependent impact.

arXiv:2609.10749v1 Announce Type: new 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.