Tobias Johansson
Hail the impossible: p-values, evidence, and likelihood
Scandinavian journal of psychology
detection internal defects seeds via nondestructive imaging techniques topic high interest optimize quality seed lots context xray imaging especially suited recent studies shown feasibility defect detection via deep learning models 3d tomography images demonstrate possibility performing deep learningbased analysis 2d xray radiography faster yet robust method via xrobustifier pipeline proposed article 2d xray images defective defectfree seeds acquired deep learning model based stateoftheart object detection neural networks proposed specific data augmentation techniques introduced compensate low ratio defects increase robustness variation physical parameters xray imaging systems seed defects accurately detected f1score 90 surpassing human performance computation time error rates robustness models principal distortions commonly found actual agroindustrial conditions demonstrated particular robustness physical noise dimensionality reduction presence seed coating work provides full pipeline automatically detect common defects seeds via 2d xray imaging method illustrated sugar beet faba bean could efficiently extended species via proposed generic xray data processing approach xrobustifier beyond simple proof feasibility constitutes important results toward effective use routine deep learningbased automatic detection seed defects