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

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.

Authors 6

Sheikh Hasan Elahi, Rusith Chamara Hathurusinghe Dewage, Habib Ullah, Muhammad Salman Siddiqui, Rakibul Islam, Fadi Al Machot

Specification

Official page: arxiv.org/abs/2609.10749
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
arXiv id: 2609.10749
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Categories: cs.CV, cs.LG
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
PDF: arxiv.org/pdf/2609.10749
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

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T19

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50 min ago

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Authors: Sheikh Hasan Elahi, Rusith Chamara Hathurusinghe Dewage, Habib Ullah

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperMeta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv
rss.arxiv.org
11 Sept 2026
New paperPaperMeta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
New paper: Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	50 min ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	50 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.