Year: 2024
Type: article
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).
Authors: Miroslav Yosifov, Thomas Lang, Virginia Florian, Stefan Gerth, Jan De Beenhouwer, Jan Sijbers, Johann Kastner, Christoph Heinzl
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $3,190
Funders: European Commission, Vlaamse regering
OpenAlex ID: W4405427372