Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI

Miroslav Yosifov, Thomas Lang, Virginia Florian, Stefan Gerth, Jan De Beenhouwer, Jan Sijbers, Johann Kastner, Christoph Heinzl

Research output: Contribution to journalArticlepeer-review

Abstract

This research explores the process of generating artificial training data for the detection and classification of defective areas in X-ray computed tomography (XCT) scans in the agricultural domain using AI techniques. It aims to determine the minimum detectability limit for such defects through analyses regarding the Probability of Detection based on analytic XCT simulations. For this purpose, the presented methodology introduces randomized shape variations in surface models used as descriptors for specimens in XCT simulations for generating virtual XCT data. Specifically, the agricultural sector is targeted in this work in terms of analyzing common degradation or defective areas in rice products. This is of special interest due to the huge biological genotypic and phenotypic variations occurring in nature. The proposed method is demonstrated on the application of analyzing rice grains for common defects (chalky and pore areas).
Original languageEnglish
Article number10
Pages (from-to)1-10
Number of pages10
JournalJournal of Nondestructive Evaluation
Volume44
Issue number1
DOIs
Publication statusPublished - 16 Dec 2024

Keywords

  • X-ray computed tomography
  • Shape variations
  • Probability of detection
  • X-ray simulation
  • Deep learning
  • Segment anything

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