ArticleFrontiers in artificial intelligence2025
Orchestrating segment anything models to accelerate segmentation annotation on agricultural image datasets.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Accurate 3D maize ear phenotyping using voxel grids derived from RGB machine vision.Plant phenomics (Washington, D.C.) · 2026Article
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Orchestrating segment anything models to accelerate segmentation annotation on agricultural image datasets.Frontiers in artificial intelligence · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 module based on segment anything models) was developed, a user interface that orchestrates the pre-labelling capabilities of both the segment anything models (SAM 1, SAM 2) and conventional annotation tools. One
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Registered trials
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