Rancang bangun bobot kartesian tiga axis untuk penyiraman tanaman yang akurat dan efisien
DOI:
https://doi.org/10.33795/eltek.v20i2.351Keywords:
Robot Kartesian, Kontrol fuzzy, Kontrol penyiramanAbstract
Untuk menunjang lahan pertanian yang subur, diperlukan proses penyiraman agar kadar air dalam tanah tetap terjaga. Kegiatan penyiraman yang dilakukan secara manual membutuhkan banyak energi. Selain itu kadar air yang diberikan dengan penyiraman manual tidak dapat terukur secara akurat. Dalam makalah ini, kami mengusulkan penyiraman otomatis dengan robot kartesian tiga aksis untuk lahan dengan ukuran 3 meter x 1.5 meter dengan 171 titik tanam. Kontrol penyiraman berbasis fuzzy agar kadar air yang diberikan bisa akurat. Sebelum penyiraman, rata-rata kelembapan tanah pada lahan tersebut adalah 45.28% dengan nilai minimal 40%, nilai maksimal 50%. Target kelembapan tanah untuk setiap titik adalah 60%. Robot dapat menyiram seluruh titik tanam tanpa campur tangan manusia. Nilai kadar air rata-rata setelah penyiraman adalah 62.10%, dengan nilai minimal 60%, nilai maksimal 65%. Selain itu, juga telah dibandingkan mekanisme penyiraman dengan metode fuzzy dengan metode on-off, metode fuzzy mampu menghasilkan penyiraman yang lebih akurat dengan tingkat kesalahan rata-rata 2.10%, sedangkan metode on-off memiliki tingkat kesalahan rata-rata 5.32% terhadap target nilai kelembapan tanah. Metode fuzzy juga lebih efisien waktu dalam penyiraman yaitu 7 detik hingga 8 detik, sedangkan metode onoff membutuhkan waktu penyiraman 10 detik hingga 15 detik.
ABSTRACT
To support fertile agricultural land, a watering process is needed so that the water content in the soil is maintained. Watering activities carried out manually require a lot of energy. In addition, the water content given by manual watering cannot be measured accurately. In this paper, we propose automatic watering with a three-axis Cartesian robot for land with a size of 3 meters x 1.5 meters with 171 planting points. Fuzzy based watering control so that the water content given can be accurate. Before watering, the average soil moisture on the land was 45.28% with a minimum value of 40%, a maximum value of 50%. The target soil moisture for each point is 60%. The robot can water the entire planting point without human intervention. The average water content value of watering is 62.10%, with a minimum value of 60%, a maximum value of 65%. In addition, also compared with the application error with the fuzzy method with the on-off
method, the fuzzy method is able to produce more accurate watering with an average error rate of 2.10%, while the on-off method has an average error of 5.32% against the soil moisture target. The fuzzy method is also more time efficient in watering, which is 7 seconds to 8 seconds, while the on-off method requires a watering time of 10 seconds to 15 seconds.
References
Martini, Ni Putu Devira Ayu, Niam Tamami, and Ali Husein Alasiry. "Design and Development of Automatic Plant Robots with Scheduling System." 2020 International Electronics Symposium (IES). IEEE, 2020.
Moraitis, Michail, Konstantinos Vaiopoulos, and Athanasios T. Balafoutis. "Design and Implementation of an Urban Farming Robot." Micromachines 13.2 (2022): 250.
Erick, M., et al. "Modeling and simulation of kinematics and trajectory planning of a farmbot Cartesian robot." 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON). IEEE, 2018.
Yeshmukhametov, Azamat, et al. "Designing of CNC based agricultural robot with a novel tomato harvesting continuum manipulator tool." International Journal of Mechanical Engineering and Robotics Research 9.6 (2020): 876-881.
Khuantham, Chatchai, and Arkira Sonthitham. "Spraying robot controlled by application smartphone for pepper farm." 2020 International Conference on Power, Energy and Innovations (ICPEI). IEEE, 2020.
Shiva, R., G. Vimal, and M. Kaviyarasu. "Intelligent Farming using Delta Robot." 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2020.
Pak, Jeonghyeon, et al. "Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms." IEEE Access (2022).
Kim, Kitae, Aarya Deb, and David J. Cappelleri. "P-AgBot: In-Row & Under-Canopy Agricultural Robot for Monitoring and Physical Sampling." IEEE Robotics and Automation Letters 7.3 (2022): 7942-7949.
Gan, H., and W. S. Lee. "Development of a navigation system for a smart farm." IFACPapersOnLine 51.17 (2018): 1-4. Kassim, A. M., et al. "Design and development of autonomous pesticide sprayer robot for fertigation farm." International Journal of Advanced Computer Science and Applications 11.2 (2020).
Hussmann, Stephan, et al. "Development and evaluation of a low-cost delta robot system for weed control applications in organic farming." 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2019.
Kiani, Farzad, et al. "Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications." Applied Sciences 12.3 (2022): 943.
Xu, Rui, and Changying Li. "A modular agricultural robotic system (MARS) for precision farming: Concept and implementation." Journal of Field Robotics 39.4 (2022): 387-409.
Tahmasebi, Mona, Mohammad Gohari, and Alireza Emami. "An Autonomous Pesticide Sprayer Robot with a Color-based Vision System." International Journal of Robotics and Control Systems 2.1 (2022): 115-123.
Sridhar Reddy, Abbireddy, V. V. M. J. Satish Chembuly, and V. V. S. Kesava Rao. "Collisionfree inverse kinematics of redundant manipulator for agricultural applications through optimization techniques." International Journal of Engineering 35.7 (2022): 1343-1354.
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Copyright (c) 2022 Niam Tamami, Hendhi Hermawan, Nofria Hanafi, Madyono Madyono, Galang Perdana, Farhan Ramadhan
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