Non-Destructive Assessment of Fruit Quality Using Image Texture Features and Minimum Distance Classification - Phongsuphap, Sukanya; Utanon, Wimon; Thongyoo, Thannicha; Utanon, Eka (Journal of Information Technology and Innovation, 2023)
This research aims to study non-destructive fruit quality assessment using guava-kimju as a case study. It presents an image processing technique by analyzing the guava’s texture features obtained from images. The study explores the attributes related to taste and flesh quality of guava. The experiments are conducted using texture features (1) Single feature, (2) Multiple features, and classification is performed using the minimum distance classification method. The results have shown that, (1) classifying the correlation between guava taste (sweet/not sweet) using the minimum distance method and the "smoothness" texture feature is more accurate than other texture features, with an accuracy rate of 66.67%. (2) Classifying the correlation between guava flesh texture (soft/not soft) using the minimum distance method and the "clumpiness" and "missibility" texture features is more accurate than other texture features, with an accuracy rate of 80.00%.