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Automated Rice Seedling Segmentation and Unsupervised Health Assessment Using Segment Anything Model with Multi-Modal

Hassan Rezvan1, Mohammad Javad Valadan Zoej1, Fahimeh Youssefi1,2

  • 1Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.

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Summary

This study introduces an automated method for segmenting rice seedlings and assessing their health using spectral and textural features. The approach enhances agricultural monitoring and supports timely interventions for increased crop yield.

Keywords:
crop growth monitoringdeep learningfeature fusionfood securityremote sensingsegment anything modelsmart agriculture

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Remote Sensing

Background:

  • Global food demand necessitates advancements in crop monitoring and management.
  • Precision agriculture requires automated tools for efficient crop assessment.

Purpose of the Study:

  • To develop a fully automated two-step method for segmenting rice seedlings and assessing their health.
  • To integrate spectral, morphological, and textural features for robust plant analysis.
  • To enable temporal monitoring of seedling health across different growth stages.

Main Methods:

  • Utilized the excess green minus excess red index for initial seedling localization.
  • Employed the Segment Anything Model (SAM) with automated point prompts for precise segmentation.
  • Extracted morphological features from masks and spectral/textural features from RGB imagery.
  • Implemented one-class Support Vector Machine (SVM) for anomaly detection to assess plant health.

Main Results:

  • Achieved mean Dice scores for segmentation ranging from 72.6 to 94.7.
  • Obtained Silhouette scores for health assessment between 0.31 and 0.44 across growth stages.
  • Demonstrated effective separation of anomalous seedlings exhibiting stress signatures.
  • Validated the framework's capability for temporal monitoring of rice seedling health.

Conclusions:

  • The proposed method offers a robust solution for automated rice seedling segmentation and health assessment.
  • Advanced segmentation and anomaly detection techniques can significantly support timely agricultural interventions.
  • Optimizing crop yield through early detection and management of unhealthy seedlings is feasible.