Park,H.Khan,M.U.Mechatronics Engineering2024-07-052024-07-0520212978-166542532-210.1109/RAAI52226.2021.95080092-s2.0-85124878894https://doi.org/10.1109/RAAI52226.2021.9508009https://hdl.handle.net/20.500.14411/4043Cherries are spring fruits enriched with nutrients, and are easily available in food markets around the world. Due to their excess demand, many enterprises solely focused on their processing. Cherries are especially susceptible to pathological-, physiological-diseases and structural degradation due to their soft outer skin. The post-harvest life of the fruit is limited by various characteristics. The agricultural industry has also been at the forefront to get benefits from the advanced machine learning tools. This study presents an image processing-based system for sorting cherries using the convolutional neural network (CNN). For this study, Prunus avium L cherries of export quality, available in Turkey, tagged as ‘0900 Ziraat’, are used. Surprisingly, there exists no dataset for these cherries; hence, we developed our dataset. Through the proposed approach based upon U-Net, the binary classification accuracy of 99% is achieved. Clear identification is demonstrated by the test results of varying mixture ratios of good and bad cherries. It can therefore be said that for cherry sorting and grading, U-Net can be applied as a reliable and promising machine learning tool. ©2021 IEEEeninfo:eu-repo/semantics/closedAccessCherry sortingConvolution neural networkMachine learningU-NetConference Object15