Design of Cartesian Type Manipulator for Automatically Capturing Plant Images Inside Greenhouse

Authors

  • I Dewa Made Subrata IPB University
  • Jacklyn Melania IPB University

DOI:

https://doi.org/10.23960/jtep-l.v12i3.545-558

Abstract

Scientific activities often require large amounts of digital image data so it is required a device capable of capturing images automatically. This study aims to design a Cartesian-type manipulator with two translational movement for capturing and storing hydroponic plant images automatically and continuously. The manipulator is programmed to capture and store plant images from 15 different positions for seven consecutive days and two cycles a day, namely at 07.00 AM and 17.00 PM. The 2020 solid work simulation yields a maximum von Mises stress of 13,783 MPa, and a minimum safety factor of 6,869. The manipulator was tested using step period treatments of 0.002, 0.003, 0.004 seconds. The best test results is treatment of 0.002 seconds with an average of x-axis and y-axis positional error was 0.380 cm and 0.076 cm, the average translation speed was 8.96 cm/second. The positioning accuracy on the x-axis and y-axis is 98.9% and 99.8%. The movement stability is quite good around the set point with an error range on the x-axis and y-axis is -0.1 to +0.9 cm and -0.065 to 0.15 cm. System response less than 1 ms and energy consumption of 16,132 watt-hours/cycle. The manipulator is able to work according to the design objectives.

 

Keywords:   Automatic, Continuous, Cartesian manipulator, Digital image, Hydroponic plants

References

Anh, N.P.T., Hoang, S., Tai, D.V., & Quoc, B.L.C. (2020). Developing robotic system for harvesting pineapples. 2020 International Conference on Advanced Mechatronic Systems (ICAMechS). https://doi.org/10.1109/ICAMechS49982.2020.9310079

Bayati, M., & Fotouhi, R. (2018). A mobile robotic platform for crop monitoring. Advances in Robotics & Automation, an Open Access Journal, 7(1), 1–7. https://doi.org/10.4172/2168-9695.1000186

Bhogavalli, R., Tech, B., & Krishnaswamy, S. (2021). Automated farming using gantry robot. International Research Journal of Engineering and Technology (IRJET), 8(7), 3547–3553. https://www.irjet.net/archives/V8/i7/IRJET-V8I7613.pdf

Chancharoen, R., Veerakiatikit, P., Kriathkungwalkai, L., Daraseneeyakul, P., Loetchaipitak, T., & Prayongrat, M. (2019). An accuracy and repeatability of a robot made with V-slot extrusion with built-in linear rails. IOP Conference Series: Materials Science and Engineering, 635(2019), 1–5. https://doi.org/10.1088/1757-899X/635/1/012025

Gao, T., Emadi, H., Saha, H., Zhang, J., Lofquist, A., Singh, A., Subramanian, B.G., Sarkar, S., Singh, A.K., & Bhattacharya, S. (2018). A novel multirobot system for plant phenotyping. Robotics, 7(4), 1–15. https://doi.org/10.3390/robotics7040061

Gong, L., Wang, W., Wang, T., & Liu, C. (2022). Robotic harvesting of the occluded fruits with a precise shape and position reconstruction approach. Journal of Field Robotics, 39(1), 1–84. https://doi.org/10.1002/rob.22041

Imran, A.I., & Kadir. (2017). Simulasi tegangan von mises dan analisa safety factor gantry crane kapasitas 3 ton. DINAMIKA Jurnal Ilmiah Teknik Mesin, 8(2), 1–4. https://doi.org/10.33772/djitm.v8i2.2378

Islami, L.A., Mardiyana, D., & Ridha, F.F. (2022). Analisis struktur aluminium profile V-slot sebagai desain rangka mesin 3d printer. Jurnal Teknik Mesin, Industri, Elektro dan Informatika (JTMEI), 1(2), 30–44. https://doi.org/10.55606/jtmei.v1i2.505

Jiang, Y., Yu, L., Jia, H., Zhao, H., & Xia, H. (2020). Absolute positioning accuracy improvement in an industrial robot. Sensors, 20(16), 1–14. https://doi.org/10.3390/s20164354

Pamungkas, G.A., Priambadi, I.G.N., & Komaladewi, A.A.I.A.S. (2020). Analisis defleksi pada rangka alat pembuat briket sampah organik. Jurnal METTEK, 6(2), 121 – 128. https://doi.org/10.24843/METTEK.2020.v06.i02.p06

Permadi, Y., Prayogo, S.S., & Kusuma, T.M. (2021). Robot edukasi pertanian agrobot-i: rancangan lektronika dan sistem penggerak. Jurnal Ilmiah Informatika Komputer, 26(1), 1–12. https://doi.org/10.35760/ik.2021.v26i1.2696

Saputra, T.W., Wijayanto, Y., Ristiyana, S., Purnamasari, I., & Muhlison, W. (2022). Non-destructive measurement of rice amylose content based on image processing and Artificial Neural Networks (ANN) model. Jurnal Teknik Pertanian Lampung, 11(2), 231–241. https://doi.org/10.23960/jtep-l.v11i2.231-241

Siskandar, R., Indrawan, N.A., Kusumah, B.R., Santosa, S.H., Irmansyah, & Irzaman. (2020). Penerapan rekayasa mesin sortir sebagai penentu kematangan buah jeruk dan tomat merah berbasis image processing. Jurnal Teknik Pertanian Lampung, 9(3), 222–236. https://doi.org/10.23960/jtep-l.v9i3.222-236

Subramanian, R., Spalding, E.P., & Ferrier, N.J. (2012). A high throughput robot system for machine vision based plant phenotype studies. Springer, 24, 619–636. https://doi.org/10.1007/s00138-012-0434-4

Sungkono, I., Irawan, H., & Patriawan, D. A. (2019). Analisis desain rangka dan penggerak alat pembulat adonan kosmetik sistem putaran eksentrik menggunakan solidwork. Prosiding Seminar Nasional Sains Dan Teknologi Terapan, 575–580. http://ejurnal.itats.ac.id/sntekpan/article/view/658

Wahid, M.I., Mustamin, S.A., & Lawi, A. (2021). Identifikasi dan klasifikasi citra penyakit daun tomat menggunakan arsitektur InceptionV4. Konferensi Nasional Ilmu Komputer (KONIK) 2021, 5(1), 257–264. https://prosiding.konik.id/index.php/konik/article/view/61

Wibawa, L.A.N., & Diharjo, K. (2019). Desain, pemilihan material, dan faktor keamanan stasiun pengisian gawai menggunakan metode elemen hingga. Jurnal Teknologi, 11(2), 97–102. https://doi.org/10.24853/jurtek.11.2.97-102

Yu, Y., Zhang, K., Liu, H., Yang, L., & Zhang, D. (2020). Real-time visual localization of the picking points for a ridge-planting strawberry harvesting robot. IEEE Access, 8(2020), 116556–116568. https://doi.org/10.1109/ACCESS.2020.3003034

Yuliany, S., Aradea, & Rachman, A.N. (2022). Implementasi deep learning pada sistem klasifikasi hama tanaman padi menggunakan metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65. https://doi.org/10.24002/jbi.v13i1.5022

Zhang, C., Gao, H., Zhou, J., Cousins, A., Pumphrey, M.O., & Sankaran, S. (2016). 3D robotic system development for high-throughput crop phenotyping. IFAC-PapersOnLine, 49(16), 242–247. https://doi.org/10.1016/j.ifacol.2016.10.045

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Published

2023-07-20