Good Apples / Bad Apples: Detection and classification of damage - Angel Star; Conor Pommer; Marius Silaghi (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
The sorting of apples in classes of quality using vision, and the detection of bad apples using X-ray and vision have naturally been addressed frequently in the past due to the sheer significance of this fruit. Our novelty consists in going past detection of damage into measuring it and classifying damaged apples for potential usages beyond direct sale, rather than simply deciding if an apple is rotten or not. Size and color of damage stains is assessed to help deciding potential usage of the apples. One can redirect apples towards various usages outside markets for the general population, such as for compost, for animal consumption, for fermentation, or for preserves pre-processing plants. Parameters of the segment anything algorithm are evaluated for this problem domain and appropriateness of algorithm features is discussed for the real problem, obtaining directions for algorithmic improvement.