Internet of Things Technology Based Agricultural Spraying Drone Design for Remote Farming Applications

Yıl 2021, , 253 - 260, 27.09.2021

Cemil Altın , Hasan Ulutaş , Eyyüp Orhan , Orhan Er , Volkan Akdoğan

https://doi.org/10.18466/cbayarfbe.781368

Cited By: 2

Öz

Internet of things and Drones are two new promising innovative technologies which is inevitable in the internet era. These technologies provide modern solutions for many fields. One of these fields is agriculture. Agriculture plays pivot role for humankind, because more than half of the World’s population depends on agriculture. In this study internet of things technology is applied to a drone which is capable for doing agricultural works like spraying, carrying and real time monitoring. An on board android device which is mount on the drone is used to manage the drone over internet by a graphical user interface software designed within the study. The farmer communicates with on board android device over internet by remote desktop application in order to manage drone and get data. The drone will help farmers by getting live data from the farm and do necessary works remotely. The aim of this study is to enable farmers to do remote farming. Agricultural activities have declined in recent years with the increase in migration from the village to the city. Thus, farmers will be able to make remote farming.

Anahtar Kelimeler

IOT, Precise Farming, Remote Farming, Smart Farming, UAV

Proje Numarası

BAP 6602b-MMF/18-194

Kaynakça

• S. K. Mohapatra, J. N. Bhuyan, P. Asundi, and A. Singh, 2016.“A Solution Framework For Managıng Internet Of Things (Iot),” Int. J. Comput. Networks Commun.; 8(6):73-87 doi: 10.5121/ijcnc.2016.8606.
• “World Employment and Social Outlook: Which sector will create the most jobs?” https://www.ilo.org/global/about-the-ilo/multimedia/maps-and-charts/WCMS_337082/lang--en/index.htm (accessed Jul. 22, 2020).
• R. Vidhya and K. Valarmathi, 2018.“Survey on Automatic Monitoring of Hydroponics Farms Using IoT,” in Proceedings of the 3rd International Conference on Communication and Electronics Systems ICCES 2018; 2018: 125-128.doi: 10.1109/CESYS.2018.8724103.
• B. Basnet and J. Bang, 2018.“The State-of-the-Art of Knowledge-Intensive Agriculture: A Review on Applied Sensing Systems and Data Analytics,” J. Sensors; 2018: 1-13. doi: 10.1155/2018/3528296.
• K. W. Jaggard, A. Qi, and E. S. Ober, 2010.“Possible changes to arable crop yields by 2050,” Philos. Trans. R. Soc. B Biol. Sci.; 365(1554): 2835-2851. doi: 10.1098/rstb.2010.0153.
• S. Chakraborty and A. C. Newton, 2011.“Climate change, plant diseases and food security: An overview,” Plant Pathology; 60(1): 2-14. doi: 10.1111/j.1365-3059.2010.02411.x.
• M. A. Jubair, S. Hossain, M. A. Al Masud, K. M. Hasan, S. H. S. Newaz, and M. S. Ahsan, 2018.“Design and development of an autonomous agricultural drone for sowing seeds,” IET Conf. Publ.; 2018(CP750): 6-9. doi: 10.1049/cp.2018.1598.
• D. Gao, Q. Sun, B. Hu, and S. Zhang, 2020.“A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles,” Sensors (Switzerland); 20(5): 1487. doi: 10.3390/s20051487.
• M. V. Suhas, S. Tejas, S. Yaji, and S. Salvi, 2018.“AgrOne: An Agricultural Drone using Internet of Things, Data Analytics and Cloud Computing Features,” 2018 4th Int. Conf. Converg. Technol. I2CT 2018; 2018: 1-6. doi: 10.1109/I2CT42659.2018.9057995.
• M. Romero, Y. Luo, B. Su, and S. Fuentes, “Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management,” Comput. Electron. Agric.; 147: 109-117. doi: 10.1016/j.compag.2018.02.013.
• M. Reinecke and T. Prinsloo, 2017.“The influence of drone monitoring on crop health and harvest size,” 2017 1st Int. Conf. Next Gener. Comput. Appl. NextComp 2017; 2017: 5-10. doi: 10.1109/NEXTCOMP.2017.8016168.
• L. G. Santesteban, S. F. Di Gennaro, A. Herrero-Langreo, C. Miranda, J. B. Royo, and A. Matese, 2017.“High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard,” Agric. Water Manag.;183:49-59, doi: 10.1016/j.agwat.2016.08.026.
• B. Allred, N. Eash, R. Freeland, L. Martinez, and D. B. Wishart, 2018.“Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study,” Agric. Water Manag.; 197:132–137, doi: 10.1016/j.agwat.2017.11.011.
• I. Wahab, O. Hall, and M. Jirström, 2018. “Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa,” Drones; 2(3): 28. doi: 10.3390/drones2030028.
• J. Huuskonen and T. Oksanen, 2018.“Soil sampling with drones and augmented reality in precision agriculture,” Comput. Electron. Agric.; 154: 25-35. doi: 10.1016/j.compag.2018.08.039.
• S. Spoorthi, B. Shadaksharappa, S. Suraj, and V. K. Manasa, “Freyr drone: Pesticide/fertilizers spraying drone - An agricultural approach, 2017.” in Proceedings of the 2017 2nd International Conference on Computing and Communications Technologies, ICCCT 2017; 2017: 252-255. doi: 10.1109/ICCCT2.2017.7972289.
• C. KOÇ, 2017.“Tarımda Pestisit Uygulama Amacıyla Ekonomik Bir Drone Tasarımı ve İmalatı,” J. Agric. Fac. Gaziosmanpasa Univ.; 34(2017-1): 94-103. doi: 10.13002/jafag4274.
• B. Dai, Y. He, F. Gu, L. Yang, J. Han, and W. Xu, “A vision-based autonomous aerial spray system for precision agriculture, 2017.” in 2017 IEEE International Conference on Robotics and Biomimetics; 2018:1–7, doi: 10.1109/ROBIO.2017.8324467
• “Shane Colton: Fun with the Complementary Filter / MultiWii.” http://scolton.blogspot.com/2012/09/fun-with-complementary-filter-multiwii.html (accessed Aug. 15, 2020).
• “Remote control – 2: Sample your remote | Jumping Jack Flashweblog.” https://jumpjack.wordpress.com/2008/05/22/remote-control-2/ (accessed Aug. 16, 2020).