Abstract
Pistachio is a healthy and delicious snack with high economic value, especially when product consists of only open pistachios. For this reason, many studies have been carried out in the literature to classify Pistachio according to whether they are open or closed. In this study, the classification process of pistachios was carried out according to whether they are open or closed using deep learning techniques. The prominent aspect of the study is that the datasets obtained with an industrial experimental set-up are used in the training of the network in order to be suitable for industrial applications and to classify it with high accuracy. In this study, AlexNet and Inception V3 structure were trained and tested with this industrial data set, the test accuracy was calculated as 96.13% and 96.54%, respectively. In order to compare the industrial data set and the desktop data set, both data sets were created. As a result of training and testing the AlexNet structure with this desktop data set, the test accuracy was calculated as 100%. When the test images from industrial dataset are fed to the network structure trained with the desktop dataset, the test accuracy was obtained as 61.75%. On the contrary, when desktop data set is fed to Alexnet structure trained with industrial data set, test accuracy is calculated as 99.84%. This clearly demonstrates how accurately the industrial dataset performs in industrial classification applications, while the desktop dataset has poor accuracy in industrial applications.
Similar content being viewed by others
Availability of data and materials
Relevant data is available from the corresponding author.
Code availability
Relevant code is available from the corresponding author.
References
S.L. Tey, R. Brown, A. Gray, A. Chisholm, C. Delahunty, Nuts improve diet quality compared to other energy-dense snacks while maintaining body weight. J. Nutr. Metab. 2011, 1–11 (2011). https://doi.org/10.1155/2011/357350
E. Aşkan, Economic analysis and marketing margin of pistachios in turkey. Bull. Natl. Res. Centre 43(1), 1–7 (2019). https://doi.org/10.1186/s42269-019-0216-5
Y.E. Ertürk, M.K. Geçer, E. Gülsoy, S. Yalçin, Antepfıstığı üretimi ve pazarlaması. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5(2), 43–62 (2015)
K. Bellitürk, M. Kuzucu, M.F. Baran, A. Çelik, Kuru koşullarda gübrelemenin verim ve kaliteye etkileri (2019). https://doi.org/10.33462/jotaf.534476
FAOSTAT. https://www.fao.org/faostat/en/ Accessed 27 Oct 2021
ITC. https://www.trademap.org/ Accessed 27 Oct 2021
T.C. Pearson, M.A. Doster, T.J. Michailides, Automated detection of pistachio defects by machine vision. Appl. Eng. Agric. 17(5), 729 (2001). https://doi.org/10.13031/2013.6905
A.E. Cetin, T.C. Pearson, A.H. Tewfik, Classification of closed-and open-shell pistachio nuts using voice-recognition technology. Trans. ASAE 47(2), 659–664 (2004). https://doi.org/10.13031/2013.16029
A. Mahmoudi, M. Omid, A. Aghagolzadeh, A.M. Borgayee, Grading of iranian’s export pistachio nuts based on artificial neural networks. Int. J. Agric. Biol. 8(3), 371–376 (2006)
M. Omid, A. Mahmoudi, M.H. Omid, An intelligent system for sorting pistachio nut varieties. Expert Syst. Appl. 36(9), 11528–11535 (2009). https://doi.org/10.1016/j.eswa.2009.03.040
M. Omid, Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier. Expert Syst. Appl. 38(4), 4339–4347 (2011). https://doi.org/10.1016/j.eswa.2010.09.103
M. Farhadi, Y. Abbaspour-Gilandeh, A. Mahmoudi, J.M. Maja, An integrated system of artificial intelligence and signal processing techniques for the sorting and grading of nuts. Appl. Sci. 10(9), 3315 (2020). https://doi.org/10.3390/app10093315
M.A. Ghazanfari, Machine vision classification of pistachio nuts using pattern recognition and neural networks. Doctoral dissertation, University of Saskatchewan (1996)
M. Yaqoob, S. Sharma, P. Aggarwal, Imaging techniques in agro-industry and their applications, a review. J. Food Meas. Charact. 15(3), 2329–2343 (2021). https://doi.org/10.1007/s11694-021-00809-w
A. Ghazanfari, J. Irudayaraj, A. Kusalik, M. Romaniuk, Machine vision grading of pistachio nuts using fourier descriptors. J. Agric. Eng. Res. 68(3), 247–252 (1997). https://doi.org/10.1006/jaer.1997.0205
K. Sabanci, M. Koklu, M.F. Unlersen, Classification of Siirt and long type pistachios (Pstacia vera L.) by artificial neural networks. Int. J. Intell. Syst. Appl. Eng. 3(2), 86 (2015). https://doi.org/10.18201/ijisae.74573
R.P. Haff, T. Pearson, Spectral band selection for optical sorting of pistachio nut defects. Trans. ASABE 49(4), 1105–1113 (2006). https://doi.org/10.13031/2013.21716
H. Nouri-Ahmadabadi, M. Omid, S.S. Mohtasebi, M.S. Firouz, Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine. Inf. Process. Agric. 4(4), 333–341 (2017). https://doi.org/10.1016/j.inpa.2017.06.002
J. Ghezelbash, A.M. Borghaee, S. Minaei, S. Fazli, M. Moradi, Design and implementation of a low cost computer vision system for sorting of closed-shell pistachio nuts. Afr. J. Agric. Res. 8(49), 6479–6484 (2013)
M. Esteki, P. Ahmadi, Y.V. Heyden, J. Simal-Gandara, Fatty acids-based quality index to differentiate worldwide commercial pistachio cultivars. Molecules 24(1), 58 (2018). https://doi.org/10.3390/molecules24010058
A. Onay, E. Tilkat, Y. Ersalı, E.A. Tilkat, V. Süzerer, Antepfıstığının (Pistacia vera L.) morfolojik ve biyolojik özellikleri ile verimini etkileyen faktörler. Batman Univ. J. Life Sci. 2(1), 116–131 (2012)
M. Kottek, J. Grieser, C. Beck, B. Rudolf, F. Rubel, World map of the köppen-geiger climate classification updated 15(3), 259–263 (2006). https://doi.org/10.1127/0941-2948/2006/0130
Global Historical Weather and Climate Data|Weather and Climate. https://tcktcktck.org/ Accessed 27 Oct 2021
TÜİK Kurumsal. https://data.tuik.gov.tr/Bulten/Index?p=Crop-Production-2020-33737 Accessed 28 Oct 2021
M.A. Yavuz, H. Yıldırım, A. Onay, Dünya antepfıstığı üretiminde son on yılın değerlendirilmesi. Batman Univ. J. Life Sci. 62(2/2), 22–31 (2016)
E.C. Tetila, B.B. Machado, G. Astolfi, N.A. de Souza Belete, W.P. Amorim, A.R. Roel, H. Pistori, Detection and classification of soybean pests using deep learning with UAV images. Comput. Electron. Agric. 179, 105836 (2020). https://doi.org/10.1016/j.compag.2020.105836
Z. Unal, Smart farming becomes even smarter with deep learning—a bibliographical analysis. IEEE Access 8, 105587–105609 (2020). https://doi.org/10.1109/access.2020.3000175
A.S.M.M. Hasan, F. Sohel, D. Diepeveen, H. Laga, M.G.K. Jones, A survey of deep learning techniques for weed detection from images. Comput. Electron. Agric. 184, 106067 (2021). https://doi.org/10.1016/j.compag.2021.106067
J. Dyson, A. Mancini, E. Frontoni, P. Zingaretti, Deep learning for soil and crop segmentation from remotely sensed data. Remote Sens. 11(16), 1859 (2019). https://doi.org/10.3390/rs11161859
P. Nevavuori, N. Narra, T. Lipping, Crop yield prediction with deep convolutional neural networks. Comput. Electron. Agric. 163, 104859 (2019). https://doi.org/10.1016/j.compag.2019.104859
A. Fuentes, S. Yoon, S. Kim, D. Park, A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017). https://doi.org/10.3390/s17092022
H. Lin, G. Zhou, A. Chen, J. Li, M. Li, W. Zhang, Y. Hu, W.T. Yu, Em-ernet for image-based banana disease recognition. J. Food Meas. Charact. 15(5), 4696–4710 (2021). https://doi.org/10.1007/s11694-021-01043-0
S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, D. Traore, Deep neural networks with transfer learning in millet crop images. Comput. Ind. 108, 115–120 (2019). https://doi.org/10.1016/j.compind.2019.02.003
X. Song, G. Zhang, F. Liu, D. Li, Y. Zhao, J. Yang, Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J. Arid Land 8(5), 734–748 (2016). https://doi.org/10.1007/s40333-016-0049-0
M.S. Sirsat, E. Cernadas, M. Fernández-Delgado, S. Barro, Automatic prediction of village-wise soil fertility for several nutrients in india using a wide range of regression methods. Comput. Electron. Agric. 154, 120–133 (2018). https://doi.org/10.1016/j.compag.2018.08.003
J. Kvam, J. Kongsro, In vivo prediction of intramuscular fat using ultrasound and deep learning. Comput. Electron. Agric. 142, 521–523 (2017). https://doi.org/10.1016/j.compag.2017.11.020
S. Yukun, H. Pengju, W. Yujie, C. Ziqi, L. Yang, D. Baisheng, L. Runze, Z. Yonggen, Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score. J. Dairy Sci. 102(11), 10140–10151 (2019). https://doi.org/10.3168/jds.2018-16164
Y. Zhang, J. Cai, D. Xiao, Z. Li, B. Xiong, Real-time sow behavior detection based on deep learning. Comput. Electron. Agric. 163, 104884 (2019). https://doi.org/10.1016/j.compag.2019.104884
B. Veeramani, J.W. Raymond, P. Chanda, DeepSort: deep convolutional networks for sorting haploid maize seeds. BMC Bioinform. 19(S9), 104884 (2018). https://doi.org/10.1186/s12859-018-2267-2
X. Meng, Y. Yuan, G. Teng, T. Liu, Deep learning for fine-grained classification of jujube fruit in the natural environment. J. Food Meas. Charact. 15(5), 4150–4165 (2021). https://doi.org/10.1007/s11694-021-00990-y
J.F. Knoll, V. Czymmek, S. Poczihoski, T. Holtorf, S. Hussmann, Improving efficiency of organic farming by using a deep learning classification approach. Comput. Electron. Agric. 153, 347–356 (2018). https://doi.org/10.1016/j.compag.2018.08.032
J. Zhang, L. Dai, F. Cheng, Corn seed variety classification based on hyperspectral reflectance imaging and deep neural network. J. Food Meas. Charact. 15(1), 484–494 (2020). https://doi.org/10.1007/s11694-020-00646-3
W. Ding, G. Taylor, Automatic moth detection from trap images for pest management. Comput. Electron. Agric. 123, 17–28 (2016). https://doi.org/10.1016/j.compag.2016.02.003
T. Kounalakis, G.A. Triantafyllidis, L. Nalpantidis, Deep learning-based visual recognition of rumex for robotic precision farming. Comput. Electron. Agric. 165, 104973 (2019). https://doi.org/10.1016/j.compag.2019.104973
V. Partel, S.C. Kakarla, Y. Ampatzidis, Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput. Electron. Agric. 157, 339–350 (2019). https://doi.org/10.1016/j.compag.2018.12.048
P. Christiansen, L. Nielsen, K. Steen, R. Jørgensen, H. Karstoft, DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16(11), 1904 (2016). https://doi.org/10.3390/s16111904
D. Rong, L. Xie, Y. Ying, Computer vision detection of foreign objects in walnuts using deep learning. Comput. Electron. Agric. 162, 1001–1010 (2019). https://doi.org/10.1016/j.compag.2019.05.019
F. Kurtulmuş, Identification of sunflower seeds with deep convolutional neural networks. J. Food Meas. Charact. 15(2), 1024–1033 (2020). https://doi.org/10.1007/s11694-020-00707-7
Y. Ampatzidis, V. Partel, B. Meyering, U. Albrecht, Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence. Comput. Electron. Agric. 164, 104900 (2019). https://doi.org/10.1016/j.compag.2019.104900
J. Ni, J. Gao, J. Li, H. Yang, Z. Hao, Z. Han, E-AlexNet: quality evaluation of strawberry based on machine learning. J. Food Meas. Charact. 15(5), 4530–4541 (2021). https://doi.org/10.1007/s11694-021-01010-9
M.N. Örnek, H. Kahramanlı, Developing a deep neural network model for predicting carrots volume. J. Food Meas. Charact. 15(4), 3471–3479 (2021). https://doi.org/10.1007/s11694-021-00923-9
B. Cho, K. Koyama, S. Koseki, Determination of ‘hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression. J. Food Meas. Charact. 15(2), 2021–2030 (2021). https://doi.org/10.1007/s11694-020-00793-7
M.L. Smith, L.N. Smith, M.F. Hansen, The quiet revolution in machine vision—a state-of-the-art survey paper, including historical review, perspectives, and future directions. Comput. Ind. 130, 103472 (2021). https://doi.org/10.1016/j.compind.2021.103472
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
J. Dean, D. Patterson, C. Young, A new golden age in computer architecture: empowering the machine-learning revolution. IEEE Micro 38(2), 21–29 (2018). https://doi.org/10.1109/mm.2018.112130030
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. in 3rd International Conference on Learning Representations, ed. Y. Bengio, Y. LeCun, ICLR 2015, San Diego, CA, USA, May 7–9, 2015. http://arxiv.org/abs/1409.1556
K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, (Springer, 2016), pp. 630–645. https://doi.org/10.1007/978-3-319-46493-0_38
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision. IEEE (2016). https://doi.org/10.1109/cvpr.2016.308
F. Chollet, Xception: deep learning with depthwise separable convolutions. IEEE (2017). https://doi.org/10.1109/cvpr.2017.195
H.K. Suh, J. Ijsselmuiden, J.W. Hofstee, E.J. van Henten, Transfer learning for the classification of sugar beet and volunteer potato under field conditions. Biosyst. Eng. 174, 50–65 (2018). https://doi.org/10.1016/j.biosystemseng.2018.06.017
M. Dyrmann, H. Karstoft, H.S. Midtiby, Plant species classification using deep convolutional neural network. Biosyst. Eng. 151, 72–80 (2016). https://doi.org/10.1016/j.biosystemseng.2016.08.024
K. Islam, R. Raj, Real-time (vision-based) road sign recognition using an artificial neural network. Sensors 17(4), 853 (2017). https://doi.org/10.3390/s17040853
J. Yang, P. Huang, F. Dai, Y. Sun, L. Wang, H. Bi, Application of deep learning in wood classification. IEEE (2019). https://doi.org/10.1109/csei47661.2019.8938960
L. Isamail, N.L. Maskuri, N.J. Isip, S.F. Lokman, M.H. Abu Bakar, Deep neural network modeling for metallic component defects using the finite element model, (Springer, 2019), pp. 259–270. https://doi.org/10.1007/978-3-030-28505-0_23
N. Silaparasetty, The tensorflow machine learning library. in Machine Learning Concepts with Python and the Jupyter Notebook Environment, pp. 149–171. Apress, (2020). https://doi.org/10.1007/978-1-4842-5967-2_8
A. Loddo, M. Loddo, C.D. Ruberto, A novel deep learning based approach for seed image classification and retrieval. Comput. Electron. Agric. 187, 106269 (2021). https://doi.org/10.1016/j.compag.2021.106269
A. Nasiri, A. Taheri-Garavand, Y.-D. Zhang, Image-based deep learning automated sorting of date fruit. Postharvest Biol. Technol. 153, 133–141 (2019). https://doi.org/10.1016/j.postharvbio.2019.04.003
Y. Zhang, L. Li, M. Ripperger, J. Nicho, M. Veeraraghavan, A. Fumagalli, Gilbreth: a conveyor-belt based pick-and-sort industrial robotics application. in 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 17–24 (2018). https://doi.org/10.1109/IRC.2018.00012
R. Pourdarbani, H.R. Ghassemzadeh, H. Seyedarabi, F.Z. Nahandi, M.M. Vahed, Study on an automatic sorting system for date fruits. J. Saudi Soc. Agric. Sci. 14(1), 83–90 (2015)
P. Chen, M. Gao, J. Huang, Y. Yang, Y. Zeng, High-speed color sorting algorithm based on fpga implementation. in 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), pp. 235–239 (2018). https://doi.org/10.1109/ISIE.2018.8433831
Acknowledgements
The authors are acknowledge and highly appreciated to Sedat Gökoğlu (Agriculture Enginner) from Gaziantep Provincial Directorate of Agriculture and Forestry Coordination Manager for his kind support, Mr. Mehmet Bostancı from Hacıbekiroğlu Food Ltd., and Haleplioğu Food Ltd. for providing pistachio samples.
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HA, TK and ZÜ. The first draft of the manuscript was written by HA and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article. We warrant that the article is the Authors’ original work. We warrant that the article has not received prior publication and is not under consideration for publication elsewhere. On behalf of all Co-authors, the corresponding Author shall bear full responsibility for the submission.
Ethical approval
Not applicable
Consent to participate
Not applicable
Consent for publication
Not applicable
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Aktaş, H., Kızıldeniz, T. & Ünal, Z. Classification of pistachios with deep learning and assessing the effect of various datasets on accuracy. Food Measure 16, 1983–1996 (2022). https://doi.org/10.1007/s11694-022-01313-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-022-01313-5