Machine learning applications to non-destructive defect detection in horticultural products

Machine learning (ML) methods have become useful tools that, in conjunction with sensing devices for quality evaluation, allow for quick and effective evaluation of the quality of food commodities based on empirical data. This review presents the recent advances in machine learning methods and their use with various sensing devices to detect defects in horticultural products. There are technical hurdles in tackling major issues around defect...