Low-Cost Classification of Close and Open Shell Antep Pistachio Nuts based on Image Analysis and Machine Learning

Yıl 2024, , 87 - 105, 31.03.2024

Abdullah Beyaz

https://doi.org/10.29133/yyutbd.1318589

Öz

The effectiveness of post-harvest industrial processes is critical to maintaining the economic worth of pistachio nuts, which play an essential role in the agricultural economy. To achieve this level of efficiency, updated applications and technology for pistachio separation and categorization are required. Different pistachio species target different markets, highlighting the need for pistachio species classification. This work aims to develop a classification model that is distinct from existing separation approaches, based on image processing and machine learning, and can provide the required categorization. A computer vision application was done to identify between three types of pistachios. A high-resolution camera was used to capture 385 images of these pistachios. The photos of the pistachio samples were processed using image processing techniques like segmentation and feature extraction. On the given dataset, an advanced classifier based on Decision Tree and Random Forest predictions was constructed, as well as a simple and successful classifier. In the research, an application with feature extraction based on the dimension and pixel measurement is proposed. The proposed approach attained a classification success rate of 100% at 70% train and 30% test, and also, 80% train and 20% test data rate with Random Forest prediction, according to the experimental data. The provided high-performance classification model fills an important demand for the separation of pistachio types while increasing the economic worth of the species.

Anahtar Kelimeler

Image Processing, Image Analysis, Classification, Gaziantep Pistachio, Decision Tree, Random Forest.

Kaynakça

• Aktaş, H., Kızıldeniz, T., & Ünal, Z. (2022). Classification of pistachios with deep learning and assessing the effect of various datasets on accuracy. Journal of Food Measurement and Characterization, 16(3), 1983-1996. https://doi.org/10.1007/s11694-022-01313-5.
• Anonymous. (2022). Different Types of Iranian Pistachios. https://ratinkhosh.com/iranian-pistachio-products/ Access date: 21.10.2022.
• Brosnan, T., & Sun, D. W. (2002). Inspection and grading of agricultural and food products by computer vision systems – A review. Computers and Electronics in Agriculture, 36, 193-213. https://doi.org/10.1016/S0168-1699(02)00101-1.
• Coban, A., Oztas Akfirat, S., & Coban, O. (2022). Economic Value of Pistachio and Production Problems. 13th Eurasian Conferences on Language and Social Sciences Abstract Book, 347-357.
• Dreher, M. L. (2012). Pistachio nuts: Composition and potential health benefits. Nutrition Reviews, 70(4), 234-240. https://doi.org/10.1111/j.1753-4887.2011.00467.x.
• Ertürk, Y. E., Geçer M. K., Gülsoy, E., & Yalçın, S. (2011). Production and Marketing of Pistachio. Journal of the Institute of Science and Technology of Igdir University, 5, 43-62.
• FAO. (2023). Pistachios in the Shell for the 2021 year. https://www.fao.org/faostat/en/#data/QCL Access date: 21.12.2023.
• Farazi, M., Abbas-Zadeh, M. J., & Moradi, H. (2017). A Machine Vision Based Pistachio Sorting Using Transferred Mid-Level Image Representation of Convolutional Neural Network. In 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP), 145-148.
• Ghazanfari, A., & Irudayaraj, D. W. J. (1998). Machine vision grading of pistachio nuts using gray-level histogram. Canadian Agricultural Engineering, 40(1), 61-66.
• Ghazanfari, A., Irudayaraj, J., & Romaniuk, M. (1997). Machine vision grading of pistachio nuts using fourier descriptors. Journal of Agricultural Engineering Research, 68(3), 247-252. https://doi.org/10.1006/jaer.1997.0205.
• Ghezelbash, J., Borghaee, A. M., Minaei, S., Fazli, S., & Moradi, M. (2013). Design and implementation of a low cost computer vision system for sorting of closed-shell pistachio nuts. African Journal of. Agricultural Research, 49(8), 6479-6484. https://doi.org/10.5897/AJAR10.1162.
• Ince, N. F., Goksu, F., Tewfik, A. H., Onaran, I., Cetin, A. E., & Pearson, T. C. (2008). Discrimination between closed and open shell (Turkish) pistachio nuts using undecimated wavelet packet transform. Biological Engineering Transactions, 1(2), 159-172. https://doi.org/10.13031/2013.24476.
• Kay, C. D., Gebauer, S. K., West, S. G., & Kris-Etherton, P. M. (2010). Pistachio increase serum antioxidants and lower serum oxidized- LDL in hypercholesterolemic adults. The Journal of Nutrition, 140(6), 1093-1098. https://doi.org/10.3945/jn.109.117366.
• Mahmoudi, A., Omid, M., & Aghagolzadeh, A. (2006). Artificial Neural Network Based Separation System for Classifying Pistachio Nuts Varieties. Proc. International Conference on Innovations in Food and Bioprocess Technologies, Thailand.
• Omid, M., Mahmoudi, A., & Omid, M. H. (2009). An intelligent system for sorting pistachio nut varieties. Expert Systems with Applications, 36(9), 11528-11535. https://doi.org/10.1016/j.eswa.2009.03.040.
• Ozkan, I. A., Koklu, M., & Saraçoglu, R. (2021). Classification of pistachio species using improved K-NN classifier. Progress in Nutrition, 23(2), 1-9, e2021044. https://doi.org/10.23751/pn.v23i2.9686.
• Pearson, T. C. (2001). Detection of pistachio nuts with closed shells using impact acoustics. Applied Engineering in Agriculture, 17(2), 249-253. https://doi.org/10.13031/2013.5450.
• Pearson, T. C., & Slaughter, D. C. (1996). Machine vision detection of early split pistachio nuts. Transactions of the ASAE, 39(3), 1203-1207. https://doi.org/10.13031/2013.27613.
• Pearson, T. C., Slaughter, D. C., & Studer, H. E. (1994). Physical properties of pistachio nuts. Transactions of the ASAE, 37(3), 913-918. https://doi.org/10.13031/2013.28159.
• Pearson, T., & Toyofuku, N. (2000). Automated sorting of pistachio nuts with closed shells. Applied Engineering in Agriculture, 16(1), 91-94. https://doi.org/10.13031/2013.4982.
• Rahimzadeh, M., & Attar, A. (2022). Detecting and counting pistachios based on deep learning. Iran Journal of Computer Science, 5(1), 69-81. https://doi.org/10.1007/s42044-021-00090-6.
• Sharma, N., & Dutta, M. (2023). Yield prediction and recommendation of crops in the northeastern region using machine learning regression models. Yuzuncu Yıl University Journal of Agricultural Sciences, 33(4), 700-708. https://doi.org/10.29133/yyutbd.1321518
• Singh, D., Taspinar, Y. S., Kursun, R., Cinar, I., Koklu, M., Ozkan, I. A., & Lee, H. N. (2022). Classification and analysis of pistachio species with pre-trained deep learning models. Electronics, 11(7), 981-995. https://doi.org/10.3390/electronics11070981.