Yurdakul, MustafaAtabaş, IrfanTaşdemir, Şakir2025-01-212025-01-212024979-835035028-9https://doi.org/10.1109/iCACCESS61735.2024.10499473https://hdl.handle.net/20.500.12587/231382024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024 -- 8 March 2024 through 9 March 2024 -- Dhaka -- 199060Chestnut(Castanea sativa) is a nutritious food with a hard outer shell. It is also used in different sectors for various purposes. Chestnut is a commercial product that is in demand worldwide due to its multi-purpose use. In order to determine the market value of chestnuts, it is necessary to classify it according to its types. With classical methods, people classify it manually. However, this method is tiring and error prone. In this study, for classifying chestnut varieties, features were extracted from chestnut images using various feature extraction methods. The extracted features were combined and classified with Linear, Poly and Radial Basis Function(RBF) kernels of Support Vector Machine(SVM). The combined handcrafted features and RBF kernel achieved an accuracy of 94.28%, precision of 93.83%, recall of 93.98%, F1-Score of 93.84%, and AUC of 99.25%. Furthermore, the most relevant features were selected using Arithmetic Optimization, Harris Hawks and Sooty Tern algorithms. The Harris Hawks Optimization selected features and RBF kernel showed the best classification performance with an accuracy of 95.84%, precision of 95.56%, recall of 95.51%, F1-score of 95.46% and AUC of 99.45%. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessArithmetic Optimization; Chestnut; Classification; Harris Hawks Optimization; Sooty Tern OptimizationConference Object10.1109/iCACCESS61735.2024.104994732-s2.0-85191939628N/A