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Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı

Year 2024, Volume: 39 Issue: 3, 1879 - 1892, 20.05.2024

Taner Gündüz Mehmet Dersuneli Yakup Kutlu

https://doi.org/10.17341/gazimmfd.1199140

Abstract

Otonom robotlar, dünya nüfus artışı karşısında azalan tarımsal üretim alanlarına ve tarımsal işgücü ihtiyacına çözüm olarak ortaya çıkıyor. Dünya genelinde insan hatalarından ve çalışma sürelerinden bağımsız bir yöntem olarak otonom hasat robotları üzerinde çalışmalar yapılmaktadır. Bu çalışmada, mobil bir platform üzerinde 6 eksenli bir robotik kol tasarlanmıştır. Derin öğrenme algoritmaları ile kamkat meyve tespiti yapılmış, özel tasarlanmış bir vakum tutucu ile entegre bir görüntü işleme algoritması oluşturulmuştur. Ayrıca literatürde hasat performansını düşüren yaprak sorunu ele alınmış ve çözüm önerilmiştir. Nesne tespiti sonrası geliştirilen algoritma ile yaprak veya herhangi bir engele takılmadan hasat gerçekleştirilmiştir. Denavit-Hartenberg (D-H) yöntemi kullanılarak elde edilen veri setinin ters kinematik hesaplamaları için yapay sinir tabanlı model oluşturularak robot hareketleri hesaplanmıştır. Nesne tespit başarısı %93 olup, saksılı kamkat ağaçlarında yapılan testler sonucunda %75 hasat başarısı elde edilmiştir.

Keywords

Otonom sistemler son efektörler makine öğrenmesi çok katmanlı algılayıcılar

References

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  • [15] Yoshida T., Kawahara T., Fukao T., Fruit Recognition Method for a Harvesting Robot with RGB-D Cameras, 2022.
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  • [27] Haykin, Simon (1998). Neural Networks: A Comprehensive Foundation (2 ed.). Prentice Hall. ISBN 0-13-273350-1.
  • [28] Šegota S. B., Anđelić N., Mrzljak V., Lorencin I., Kuric I., Car Z., Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator, International Journal of Advanced Robotic Systems, 18(4), 2021.
  • [29] Camero A, Toutouh J, Alba E., A specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks, arXiv:1909.02425, 1–10, 2019.
  • [30] Bradski G., Kaehler A., Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media, Sebastopol, 1-580, 2008.
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  • [32] Zhao Y., Gong L., Huang Y., Liu C., A review of key techniques of vision-based control for harvesting robot, Computers and Electronics in Agriculture, 127, 311-323, 2016.

Year 2024, Volume: 39 Issue: 3, 1879 - 1892, 20.05.2024

Taner Gündüz Mehmet Dersuneli Yakup Kutlu

https://doi.org/10.17341/gazimmfd.1199140

Abstract

References

  • [1] Odegard I. Y. R., Van der Voet E., The future of food Scenarios and the effect on natural resource use in agriculture in 2050, Ecological Economics, 97, 51-59, 2014.
  • [2] Iqbal J., Islam R. U., Abbas S. Z., Khan A. A., Ajwad S. A., Automatzacija industrijskih poslova kroz mehatroničke sustave pregled robotike iz industrijske perspective, Tehnički vjesnik, 23(3), 917-924, 2016.
  • [3] Hassan M. U., Ullah M., Iqbal J., Towards autonomy in agriculture: Design and prototyping of a robotic vehicle with seed selector, 2nd International Conference on Robotics and Artificial Intelligence ICRAI, pp. 37-44, IEEE, 2016.
  • [4] Tanigaki K., Fujiura T., Akase A., Imagawa J., Cherry-harvesting robot, Computers and electronics in agriculture, 63(1), 65-72, 2008.
  • [5] Almendral K. A. M., Babaran R. M. G., Carzon B. J. C., Cu K. P. K., Lalanto J. M., Abad A. C., Autonomous fruit harvester with machine vision. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-6), 79-86, 2018.
  • [6] Feng Q., Zou W., Fan P., Zhang C., Wang X., Design and test of robotic harvesting system for cherry tomato, International Journal of Agricultural and Biological Engineering, 11(1), 96-100, 2018.
  • [7] Luo L., Tang Y., Lu Q., Chen X., Zhang P., A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard, Computers in industry, 99, 130-139, 2018.
  • [8] Xiong Y., Peng C., Grimstad L., From P. J., Isler V., Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper, Computers and electronics in agriculture, 157, 392-402, 2019.
  • [9] Williams H. A., Jones M. H., Nejati M., Seabright M. J., Bell J., Penhall N. D., MacDonald B. A., Robotic kiwifruit harvesting using machine vision, convolutional neural networks and robotic arms, Biosystems Engineering, 181, 140-156, 2019.
  • [10] Onishi Y., Yoshida T., Kurita H., Fukao T., Arihara H., Iwai A., An automated fruit harvesting robot by using deep learning, Robomech Journal, 6(1), 1-8, 2019.
  • [11] Arad B., Balendonck J., Barth R., Ben Shahar O., Edan Y., Hellström T., van Tuijl B., Development of a sweet pepper harvesting robot, Journal of Field Robotics, 37(6), 1027-1039, 2020.
  • [12] Zhang K., Lammers K., Chu P., Li Z., Lu R., System design and control of an apple harvesting robot, Mechatronics, 79, 102644, 2021.
  • [13] Yin W., Wen H., Ning Z., Ye J., Dong Z., Fruit Detection and Pose Estimation for Grape Cluster Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks, Frontiers in Robotics and AI, 8, 2021.
  • [14] Jun J., Kim J., Seol J., Kim J., Son H. I., Towards an Efficient Tomato Harvesting Robot: 3D Perception, Manipulation, and End-Effector, IEEE Access, 9, 17631-17640, 2021.
  • [15] Yoshida T., Kawahara T., Fukao T., Fruit Recognition Method for a Harvesting Robot with RGB-D Cameras, 2022.
  • [16] Wan H., Fan Z., Yu X., Kang M., Wang P., A real-time branch detection and reconstruction mechanism for harvesting robot via convolutional neural network and image segmentation, Computers and Electronics in Agriculture, 192, 106609, 2022.
  • [17] Dumitrache A., Robot kinematics diagram http://alexdu.github.io/sketch-lib, 2010, Erişim tarihi: 12.07.2021.
  • [18] Bochkovskiy A., Wang C. Y., Liao H. Y. M., Yolov4: Optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934, 2020.
  • [19] Zhu L., Geng X., Li Z., Liu C., Improving yolov5 with attention mechanism for detecting boulders from planetary images. Remote Sensing, 13(18), 3776, 2021.
  • [20] WEB (a), Overview of model structure about YOLOv5, https://github.com/ultralytics/yolov5/issues/280, 2020, Erişim tarihi: 25.06.2021
  • [21] Li C., Li L., Jiang H., Weng K., Geng Y., Li L., Ke Z., Li Q., Cheng M., Nie M., Li Y., Zhang B., Liang Y., Zhou L., Xu X., Chu X., Wei X., Wei X., YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications, arXiv preprint arXiv:2209.02976, 2022.
  • [22] Solawetz J., Nelson J., What's New in YOLOv6?, https://blog.roboflow.com/yolov6/, 2022, Erişim Tarihi: 20.08.2022
  • [23] Yayik A., Kutlu Y., Diagnosis of congestive heart failure using poincare map plot, 20th Signal Processing and Communications Applications Conference (SIU), Muğla, 1-4, 2012.
  • [24] Shao C., A quantum model for multilayer perceptron. arXiv preprint arXiv:1808.10561, 2018.
  • [25] Altınkaynak A., Ağsız Yöntem Uygulamaları için Trigonometri Tabanlı Radyal Özelliğe Sahip Yeni Bir Temel Fonksiyon, International Journal of Advances in Engineering and Pure Sciences 32.1, 96-110, 2020.
  • [26] Huang G.B., Qin-Yu Z., Chee-Kheong S., Extreme learning machine: theory and applications, Neurocomputing 70.1-3, 489-501, 2006.
  • [27] Haykin, Simon (1998). Neural Networks: A Comprehensive Foundation (2 ed.). Prentice Hall. ISBN 0-13-273350-1.
  • [28] Šegota S. B., Anđelić N., Mrzljak V., Lorencin I., Kuric I., Car Z., Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator, International Journal of Advanced Robotic Systems, 18(4), 2021.
  • [29] Camero A, Toutouh J, Alba E., A specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks, arXiv:1909.02425, 1–10, 2019.
  • [30] Bradski G., Kaehler A., Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media, Sebastopol, 1-580, 2008.
  • [31] Marcmateo, BCN3D Technologies. https://github.com/BCN3D, 2018, Erişim tarihi: 31.04.2021.
  • [32] Zhao Y., Gong L., Huang Y., Liu C., A review of key techniques of vision-based control for harvesting robot, Computers and Electronics in Agriculture, 127, 311-323, 2016.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Taner Gündüz 0000-0002-0361-5612

Mehmet Dersuneli 0000-0002-9689-2554

Yakup Kutlu 0000-0002-9853-2878

Early Pub Date May 16, 2024
Publication Date May 20, 2024
Submission Date November 3, 2022
Acceptance Date December 9, 2023
Published in Issue Year 2024 Volume: 39 Issue: 3

Cite

APA Gündüz, T., Dersuneli, M., & Kutlu, Y. (2024). Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(3), 1879-1892. https://doi.org/10.17341/gazimmfd.1199140
AMA Gündüz T, Dersuneli M, Kutlu Y. Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı. GUMMFD. May 2024;39(3):1879-1892. doi:10.17341/gazimmfd.1199140
Chicago Gündüz, Taner, Mehmet Dersuneli, and Yakup Kutlu. “Kamkat Meyvesi için Derin öğrenmeye Dayalı Otonom Hasat Robotu tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 3 (May 2024): 1879-92. https://doi.org/10.17341/gazimmfd.1199140.
EndNote Gündüz T, Dersuneli M, Kutlu Y (May 1, 2024) Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 3 1879–1892.
IEEE T. Gündüz, M. Dersuneli, and Y. Kutlu, “Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı”, GUMMFD, vol. 39, no. 3, pp. 1879–1892, 2024, doi: 10.17341/gazimmfd.1199140.
ISNAD Gündüz, Taner et al. “Kamkat Meyvesi için Derin öğrenmeye Dayalı Otonom Hasat Robotu tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/3 (May 2024), 1879-1892. https://doi.org/10.17341/gazimmfd.1199140.
JAMA Gündüz T, Dersuneli M, Kutlu Y. Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı. GUMMFD. 2024;39:1879–1892.
MLA Gündüz, Taner et al. “Kamkat Meyvesi için Derin öğrenmeye Dayalı Otonom Hasat Robotu tasarımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 3, 2024, pp. 1879-92, doi:10.17341/gazimmfd.1199140.
Vancouver Gündüz T, Dersuneli M, Kutlu Y. Kamkat meyvesi için derin öğrenmeye dayalı otonom hasat robotu tasarımı. GUMMFD. 2024;39(3):1879-92.