A CNN-Based System for Classification and Real-Time Detection of Raisin

Although Turkey ranks first in the world in terms of raisin exports, it ranks fourth in revenue.The main reason for this problem is that the quality of exported raisins does not meet the desired level.In this study, an intelligent real-time system was developed to determine suitability for export by classifying raisins according to color and capstem amount according to the criteria of TS 3411.A dataset of 2336 Sultana raisins was created and expanded to 10,544 images using data augmentation techniques such as exposure adjustment, rotation, and noise addition.These techniques not only increased the dataset size but also significantly improved the model performance, as evidenced by the remarkable success of the CNN models with an average mAP of 98.8%.The images were evaluated in real time in the developed GUI software and their compliance with the standard was determined.The study is the first of its kind to classify and detect raisins according to the standard TS 3411 using an intelligent real-time system.