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Orchestrating segment anything models to accelerate segmentation annotation on agricultural image datasets

Increasingly many applications of machine vision and artificial intelligence (AI) can be observed in agriculture. Yet, high-quality training data remains a bottleneck in the development of many AI solutions, particularly for image segmentation. Therefore, ARAMSAM (agricultural rapid annotation modul...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Leon H. Oehme, Jonas Boysen, Zhangkai Wu, Anthony Stein, Joachim Müller
Format: Artigo
Sprache:Inglês
Veröffentlicht: Frontiers Media S.A. 2026-01-01
Schriftenreihe:Frontiers in Artificial Intelligence
Schlagworte:
agriculture
annotation
deep learning
phenotyping
segment anything model 2
segmentation
Online-Zugang:https://www.frontiersin.org/articles/10.3389/frai.2025.1748468/full
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