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 detection in fruit and vegetables as well as various other food items, such as achieving fast, early and quantitative assessments. The role that ML methods have played towards addressing such issues are reviewed, the present limitations highlighted, and future prospects identified.