Emre AVUÇLU

Classification Of Pistachio Images With The ResNet Deep Learning Model

Pistachio, which is grown in many parts of the world today, has an important place in the agricultural economy. In order to maintain this economic value, the post-harvest industrial classification process is very important to obtain efficiency from this harvest. In the process of separating pistachios, an efficient classification process is needed in order for different pistachio species to appeal to different markets. For this reason, the classification process of pistachios is very important. In this study, Kirmizi and Siirt pistachio classification with 2148 images was made using ResNet architecture. After the statistical experimental studies, the highest classification accuracy was obtained from fold-1 as 88.5781% and the Accuracy value was 0.86168 after the classification process.

Keywords:

Pistachio Classification, Deep Learning ResNet,

PDF

___

Anonymous (2022). UN Food and Agriculture Organization, Corporate Statistical Database, http://www.fao.org/faostat/en/#data/QC, [Visit Date: 24.02.2022].
Anonymous (2020). The U.S.-Iran pistachio war is heating up, https://web.archive.org/web/20201101023924/https://www.bloomberg.com/news/articles/2020-02-21/the-u-s-iran-pistachio-war-is-heating-up, [Visit Date: 24.02.2020].
Anonymous (2021). Iranian pistachios face tough competition from the US, https://www.tridge.com/stories/iranian-pistachios-face-tough-competition-from-the-us, [Visit Date: 24.02.2021].
Anonymous (2014). Antep fıstığı lideri: Şanlıurfa, https://web.archive.org/web/20200423190059/https://www.trthaber.com/haber/ekonomi/antep-fistigi-lideri-sanliurfa-135688.html/amp, [Visit Date: 11.02.2023].
Anonymous (2019). Tarım ve Orman Bakanlığı, zararlı yok, verim kaybı yok, alternatifi yok. Antep Fıstığı Araştırma Dergisi, 7.
Dreher ML (2012). Pistachio nuts: composition and potential health benefits. Nutrition Reviews 70(4): 234–240. Kay CD, Gebauer SK, West SG, Kris-Etherton PM (2010). Pistachios increase serum antioxidants and lower serum oxidized-LDL in hypercholesterolemic adults. The Journal of Nutritio, 140(6): 1093-1098.
Ertürk YE, Geçer MK, Gülsoy E, Yalçın S (2011). Production and marketing of pistachio. Journal of the Institute of Science and Technology of Igdir University 5: 43-62.
Ozkan IA, Koklu M, Saracoglu R (2021). Classification of pistachio species using improved K-NN classifier. Progress in Nutrition, 23: 2.
Tunalıoğlu R, Taşkaya B (2003). Antepfıstığı. Tarımsal Ekonomi Araştırma Enstitüsü Dergisi.
Cetin AE, Pearson TC, Tewfik AH (2004). Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, V-677.
Casasent DA, Sipe MA, Schatzki TF, Keagy PM, Lee LC (1998). Neural net classification of X-ray pistachio nut data. LWT- Food Science and Technology, 31(2): 122-128.
Atay Ü (2007). The investigation of classification systems used for pistahio and construction of an alternetive classification system. Ph.D. Thesis, Harran University (Unpublished), Turkey.
Abbaszadeh M, Rahimifard A, Eftekhari M, Zadeh HG, Fayazi A, Dini A, Danaeian M (2019). Deep Learning-Based Classification of the Defective Pistachios via Deep Autoencoder Neural Networks, arXiv:1906.11878.
Rahimzadeh M, Attar A (2021). Detecting and counting pistachios based on deep learning. Iran Journal of Computer Science, 5: 69–81.
Dini A, Zadeh HG, Rahimifard A, Fayazi A, Eftekhari M, Abbaszadeh M (2020). Designing a hardware system to separate defective pistachios from healthy ones using deep neural networks. Iranian Journal of biosystems Engineering, 51: 149–159.
Sıngh D, Taspınar YS, Kursun R, Cınar I, Koklu M, Ozkan IA, Lee HN (2022). Classification and analysis of pistachio species with pre-trained deep learning models. Electronics,11 (7): 981.
Kaya V (2021). Otonom güvenlik kontrollerinde kullanılmak üzere derin öğrenme tabanlı silah tespit ve tanıma sistemi. Ph.D. Thesis, Fırat University (Unpublished), Turkey.
He K, Zhang X, Ren S, Sun J (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas.
Kaya V, Tuncer S, Baran A (2020). Derı̇n öğrenme yöntemlerı̇ kullanılarak nesne tanıma. International Science and Technology Conference (ISTEC), Kıbrıs.
Toğaçar M, Ergen B (2019). Biyomedikal görüntülerde derin öğrenme ile mevcut yöntemlerin kıyaslanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 31(1):109-121