Apricot Position Determination Using Deep Learning for Apricot Stone Extraction Machine

Document Type : Original Research

Authors
Ö. O. Dursun 1
S. Toraman 2
Y. Er 3
E. Oksuztepe 1
1 Avionics Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.
2 Air Traffic Control Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.
3 Airframes and Powerplants Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.
10.22034/jast.25.3.595
Abstract
Despite the developing technology, extraction of Sulfured Dried Apricot (Prunus armeniaca) (SDA) stones is still done manually and thus requires a significant amount of labor and time and also causes serious problems in terms of hygiene. According to International Food Standards (CXS 130-1981) and Turkish Standard 485, the SDA stones must be extracted from the peduncle side of the apricot. Therefore, the correct position of the apricot peduncle and style side must be determined. In this study, a deep learning architecture was improved for the first time to determine the position of SDA stones as a component of the agricultural machine developed to extract SDA stones. In this study, a new Capsule Network architecture was used. With the original capsule network, SDA images were classified with 86.23% accuracy, while it increased to 94.47%with the improved capsule network. Also, the processing time of the developed network architecture was about twice as fast as the original. The result clearly demonstrates that the SDA stone positions are easily determined. Therefore, the designed agricultural machine can extract the SDA stones hygienically and rapidly, without any need for human power.

Keywords

Subjects

1. da Costa, A.Z., Figueroa, H.E.H., Fracarolli, J.A., 2020. Computer vision based detection of external defects on tomatoes using deep learning. Biosyst. Eng., 190: 131–144. https://doi.org/10.1016/j.biosystemseng.2019.12.003
2. Daş, B., Toraman, S., Türkoǧlu, I., 2020. A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks. Turkish J. Electr. Eng. Comput. Sci., 28: 1932–1948. https://doi.org/10.3906/ELK-1909-119
3. Dombetzki, L.A., 2018. An overview over Capsule Networks Luca. Semin. FI / IITM SS 18, Netw. Archit. Serv., 105–112. https://doi.org/10.2313/NET-2018-11-1
4. Faisal, M., Albogamy, F., Elgibreen, H., Algabri, M., Alqershi, F.A., 2020. Deep Learning and Computer Vision for Estimating Date Fruits Type, Maturity Level, and Weight. IEEE Access, 8: 206770–206782. https://doi.org/10.1109/ACCESS.2020.3037948
5. FAO, 2021. Food and Agriculture Organization of the United Nations 2021. https://www.fao.org/faostat/en/#data (accessed 1.3.22).
6. IFS, 2022. International Food Standarts 2019. https://www.fao.org/fao-who-codexalimentarius/sh-proxy/en/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCXS%2B130-1981%252FCXS_130e.pdf (accessed 1.3.22).
7. INC, 2021. International Nut&Dried Fruit 2021. https://www.nutfruit.org/files/tech/1625230833_INC_Stats_2021.pdf (accessed 1.3.22).
8. Karabacak, T., Uzundumlu, A., 2020. Apricot Production Forecasts of the Leading Provinces in 2019-2025 Periods. IBAD J. Soc. Sci., 561–573. https://doi.org/10.21733/ibad.796324
9. Khojastehnazhand, M., Mohammadi, V., Minaei, S., 2019. Maturity detection and volume estimation of apricot using image processing technique. Sci. Hortic. (Amsterdam)., 251: 247–251. https://doi.org/10.1016/j.scienta.2019.03.033
10. Lukic, V., Brüggen, M., Mingo, B., Croston, J.H., Kasieczka, G., Best, P.N., 2019. Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks. Mon. Not. R. Astron. Soc., 487: 1729–1744. https://doi.org/10.1093/mnras/stz1289
11. Mao, S., Li, Y., Ma, Y., Zhang, B., Zhou, J., Kai Wang, 2020. Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion. Comput. Electron. Agric., 170: 105254. https://doi.org/10.1016/j.compag.2020.105254
12. Mirnezami, S.V., HamidiSepehr, A., Ghaebi, M., Hassan-Beygi, S.R., 2020. Apricot Variety Classification Using Image Processing and Machine Learning Approaches, in: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing. ACM, New York, NY, USA, pp. 1–6. https://doi.org/10.1145/3448823.3448856
13. Mureşan, H., Oltean, M., 2018. Fruit recognition from images using deep learning. Acta Univ. Sapientiae, Inform., 10: 26–42. https://doi.org/10.2478/ausi-2018-0002
14. Naik, S., Patel, B., 2017. Machine Vision based Fruit Classification and Grading - A Review. Int. J. Comput. Appl., 170: 22–34. https://doi.org/10.5120/ijca2017914937
15. Örnek, M.N., Hacıseferoğulları, H., 2020. Design of Real Time Image Processing Machine for Carrot Classification, YYU J AGR SCI 30(2): 355-366. doi.org/10.29133/yyutbd.685425
16. Örnek, M.N., Örnek, H.K., 2021, Developing a deep neural network model for predicting carrots volume. Food Measure 15, 3471–3479. https://doi.org/10.1007/s11694-021-00923-9
17. Özdemir, İ.S., Öztürk, B., Çelik, B., Sarıtepe, Y., Aksoy, H., 2018. Rapid, simultaneous and non-destructive assessment of the moisture, water activity, firmness and SO2 content of the intact sulphured-dried apricots using FT-NIRS and chemometrics. Talanta, 186: 467–472. https://doi.org/10.1016/j.talanta.2018.05.007
18. Parviz, L., 2020. Comparative evaluation of hybrid sarıma and machine learning techniques based on time varying and decomposition of precipitation time series. J. Agr. Sci. Tech., 22(2): 563-578.
19. Ropelewska, E., Sabanci, K., Aslan, M.F., 2022. Preservation effects evaluated using innovative models developed by machine learning on cucumber flesh, Eur Food Res Technol. https://doi.org/10.1007/s00217-022-04016-9
20. Sabour, S., Frosst, N., Hinton, G.E., 2017. Dynamic Routing Between Capsules. arXiv, 1710.09829 , 2017.
21. Singh, D., Taspinar, Y.S., Kursun, R., Cinar, I., Koklu, M., Ozkan, I.A., Lee, H., 2022. Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Methods, Electronics. 11(7): 981. doi.org/10.3390/electronics11070981
22. Shamim Hossain, M., Al-Hammadi, M., Muhammad, G., 2019. Automatic Fruit Classification Using Deep Learning for Industrial Applications. IEEE Trans. Ind. Informatics, 15: 1027–1034. https://doi.org/10.1109/TII.2018.2875149
23. Tang, Y., Zhu, M., Chen, Z., Wu, C., Chen, B., Li, C., Li, L., 2022. Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method. Structures 37: 426-441. https://doi.org/10.1016/j.istruc.2021.12.055
24. Toraman, S., Dursun, Ö.O., 2021. GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks. Traitement du Signal, 38(6): 1689-1698. https://doi.org/10.18280/ts.380612
25. Toraman, S., Alakus, T.B., Turkoglu, I., 2020. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons and Fractals, 140: 110122. https://doi.org/10.1016/j.chaos.2020.110122
26. Toraman, S., Türkoğlu, İ., 2020. A new method for classifying colon cancer patients and healthy people from FTIR signals using wavelet transform and machine learning techniques. J. Fac. Eng. Archit. Gazi Univ., 35: 933–942. https://doi.org/10.17341/gazimmfd.564803
27. TS, 2022. Turkish Standards Institution. 2013. https://intweb.tse.org.tr/Standard/Standard/StandardAra.aspx (accessed 1.3.22).
28. Türkoğlu, M., Hanbay, K., Sivrikaya, I.S., Hanbay, D., 2020. Classification of Apricot Diseases by using Deep Convolution Neural Network. BEU J. Sci., 9: 334–345.
29. Wu F, Duan J, Chen S, Ye Y, Ai P and Yang Z, 2021. Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point. Front. Plant Sci. 12:705021. doi: 10.3389/fpls.2021.705021
30. Yang, X., Zhang, R., Zhai, Z., Pang, Y., Jin, Z., 2019. Machine learning for cultivar classification of apricots (Prunus armeniaca L.) based on shape features. Sci. Hortic. (Amsterdam)., 256: 108524. https://doi.org/10.1016/j.scienta.2019.05.051
31. Zhang, H., Chan, W.K., 2019. Apricot: A weight-adaptation approach to fixing deep learning models. Proc. - 2019 34th IEEE/ACM Int. Conf. Autom. Softw. Eng. ASE 2019 376–387. https://doi.org/10.1109/ASE.2019.00043
32. Zhu, L., Spachos, P., Pensini, E., Plataniotis, K.N., 2021. Deep learning and machine vision for food processing: A survey. Curr. Res. Food Sci., 4: 233–249. https://doi.org/10.1016/j.crfs.2021.03.009
Journal of Agricultural Science and Technology
Volume 25, Issue 3
May and June 2023
Pages 595-607