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

Leon H Oehme1, Jonas Boysen2, Zhangkai Wu2

  • 1Institute of Agricultural Engineering, Tropics and Subtropics Group, University of Hohenheim, Stuttgart, Germany.

Frontiers in Artificial Intelligence
|February 9, 2026
PubMed
Summary

ARAMSAM accelerates AI-driven agricultural image segmentation by integrating Segment Anything Models (SAMs). This tool significantly reduces annotation time, making AI development faster and more efficient for agricultural applications.

Keywords:
UAVagricultureannotationdeep learningphenotypingsegment anything model 2segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Agricultural Technology

Background:

  • High-quality training data is crucial for AI development, especially for image segmentation tasks in agriculture.
  • Existing annotation methods are time-consuming, creating a bottleneck for AI solutions.

Purpose of the Study:

  • To develop ARAMSAM, a user interface that integrates Segment Anything Models (SAMs) and conventional tools for rapid agricultural image annotation.
  • To evaluate the performance of SAMs and the efficiency of ARAMSAM in reducing annotation times.

Main Methods:

  • Developed ARAMSAM, a user interface orchestrating SAMs (SAM 1, SAM 2) and annotation tools.
  • Conducted in silico experiments on SAM performance and hyperparameter optimization of Automatic Mask Generators (AMG).
  • Performed a user experiment with agricultural experts to quantify annotation time reduction.

Main Results:

  • Hyperparameter optimization significantly improved SAM 2's F2-score from 0.05 to 0.74.
  • SAM 1's F2-score improved from 0.87 to 0.93 after optimization.
  • Annotation time reduced to 1.6-2.1 seconds per mask using ARAMSAM, compared to 9.7 seconds for polygon drawing.

Conclusions:

  • ARAMSAM effectively leverages SAMs to accelerate segmentation mask annotation in agriculture.
  • The developed tool demonstrates significant potential for improving AI development efficiency in various fields.
  • ARAMSAM will be released as open-source software.