首页|Research on Machine Learning Published by a Researcher at Baekdudaegan National Arboretum (Non-Destructive Seed Viability Assessment via Multispectral Imaging a nd Stacking Ensemble Learning)
Research on Machine Learning Published by a Researcher at Baekdudaegan National Arboretum (Non-Destructive Seed Viability Assessment via Multispectral Imaging a nd Stacking Ensemble Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from the Baekdudaegan Natio nal Arboretum by NewsRx journalists, research stated, “The tetrazolium (TZ) test is a reliable but destructive method for identifying viable seeds.” Funders for this research include R&D Program For Forest Science Te chnology. Our news journalists obtained a quote from the research from Baekdudaegan Nation al Arboretum: “In this study, a non-destructive seed viability analysis method f or Allium ulleungense was developed using multispectral imaging and stacking ens emble learning. Using the Videometerlab 4, multispectral imaging data were colle cted from 390 A. ulleungense seeds subjected to NaCl-accelerated aging treatment s with three repetitions per treatment. Spectral values were obtained at 19 wave lengths (365-970 nm), and seed viability was determined using the TZ test. Next, 80% of spectral values were used to train Decision Tree, Random F orest, LightGBM, and XGBoost machine learning models, and 20% were used for testing. The models classified viable and non-viable seeds with an acc uracy of 95-91% on the K-Fold value (n = 5) and 85-81% on the test data. A stacking ensemble model was developed using a Decision Tree as the meta-model, achieving an AUC of 0.93 and a test accuracy of 90% .”
Baekdudaegan National ArboretumCyborgsEmerging TechnologiesMachine Learning
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