Design and implementation of a novel seeding drone

Year 2025, Volume: 43 Issue: 1, 189 - 198, 28.02.2025

Simge Karademir Şakir Teter Yusuf Deniz Ferhat Yildiz İsmail Yariçi

Abstract

One of the most important environmental problems that the world facing is undoubtedly the deforestation problem, especially due to forest fires. Early detection and prevention of fires, extinguishing them before they grow, and reforestation in burned areas are widely studied solutions to deforestation. However, the number of technological studies related to reforesta-tion is limited compared to the other fields. The main purpose of this study is to develop a 4-winged (Quadcopter) seeding Unmanned Aerial Vehicle (UAV) to be used in the reforesta-tion of hard-to-reach areas. This tool can also be used in seeding agricultural lands. In the design of the drone, to achieve high-quality flight performance, engines with a thrust power that able to lift at least twice its own weight and the Pixhawk flight control card to ensure the autonomous tracking of routes are preferred. F450 drone frame with 9045 propellers is used in the study. Moreover, a completely unique seeding system and a suitable landing gear are designed and fabricated. Operation of the seeding system is realized using Arduino Nano and a step motor. In the tests, our UAV device and the seeding system operated smoothly, and it was indicated that drones could achieve reforestation in hard-to-reach areas to prevent deforestation. This research can be further developed for seed dispersal in other agricultural applications and for larger areas.

Keywords

Deforestation, Seeding, UAV, Quadcopter

References

• REFERENCES
• [1] Trigueiro WR, Nabout JC, Tessarolo G. Uncovering the spatial variability of recent deforestation drivers in the Brazilian Cerrado. J Environ Manag 2020;275:111243.
• [2] Semper-Pascual A, Decarre J, Baumann M, Busso JM, Camino M, Gomez-Valencia B, et al. Biodiversity loss in deforestation frontiers: linking occupancy modelling and physiological stress indicators to understand local extinctions. Biol Conserv 2019;236:281–288.
• [3] Fearnside PM. Consequencias do desmatamento da Amazionia. Scientific American 2010;3:54–59.
• [4] Silva RM, Lopes AG, Santos CAG. Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature. J Environ Manag 2023;326:116664.
• [5] Amigo I. When will the Amazon hit a tipping point? Nature 2020;578:505–507.
• [6] Allen JC, Barnes DF. The causes of deforestation in developing countries. Ann Assoc Am Geogr 1985;75:163–184.
• [7] MacCarthy J, Tyukavina S, Weisse M, Harris N. New Data Confirms: Forest Fires Are Getting Worse. 2022; https://www.wri.org/insights/global-trends-forest-fires Accessed on Jan 27, 2025.
• [8] Mataveli GAV, Chaves MED, Brunsell NA, Aragão LEOC. The emergence of a new deforestation hotspot in Amazonia. Perspect Ecol Conserv 2021;19:33–36.
• [9] Alkhatib AA, A review on forest fire detection techniques. Intl J Distrib Sensor Netw 2014;10:597368.
• [10] Rathee M, Dahiya J. Detection techniques of forest fire: Using WSNs. World J Adv Eng Technol Sci 2023;9:387–390.
• [11] Aslan YE, Korpeoglu I, Ulusoy Ö. A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput Environ Urban Syst 2012;36:614–625.
• [12] Abdusalomov AB, Islam BM, Nasimov R, Mukhiddinov M, Whangbo TK. An improved forest fire detection method based on the detectron2 model and a deep learning approach, Sensors 2023;23:1512.
• [13] Kalaivani V, Chanthiya P. A novel custom optimized convolutional neural network for a satellite image by using forest fire detection. Earth Sci Inform 2022;15:12851295.
• [14] Dampage U, Bandaranayake L, Wanasinghe R, Kottahachchi K, Jayasanka B. Forest fire detection system using wireless sensor networks and machine learning. Sci Rep 2022;12:46.
• [15] Mahmudnia D, Arashpour M, Bai Y, Feng H. Drones and blockchain integration to manage forest fires in remote regions. Drones 2022;6:331.
• [16] Lawrence ID, Agnishwar J, Vijayakumar R. Revolutionizing firefıghting: An experimental journal on the design and performance of drones in fire suppression. Eur Chem Bull 2023;12(Suppl 12):12381252.
• [17] Innocente MS, Grasso P. Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems. J Comput Sci 2019;34:80–101.
• [18] Miranda VRF, Rezende AMC, Rocha TL. Autonomous navigation system for a delivery drone. J Control Autom Electr Syst 2022;33:141–155.
• [19] Albayrak E, Vural H, Ekici M, Ekici S. TRIZ approach to the relevant problem for the transport of assay tubes: A case study on family health centers in Amasya. Sigma J Eng Nat Sci 2023;41:900906.
• [20] Bisio I, Garibotto C, Haleem H, Lavagetto F, Sciarrone A. A systematic review of drone based road traffic monitoring system. IEEE Access 2022;10:101537101555.
• [21] Choi HW, Kim HJ, Kim SK, Na WS. An overview of drone applications in the construction industry. Drones 2023;7:515.
• [22] Shvetsova SV, Shvetsov AV. Ensuring safety and security in employing drones at airports. J Transport Secur 2021;14:4153.
• [23] Honarmand M, Shahriari H. Geological mapping using drone-based photogrammetry: An application for exploration of vein-type Cu mineralization. Minerals 2021;11:585.
• [24] Janney JB, Premkumar J, Krishnakumar S, Shivani SA, Atchaya E, Kanmani PG, Air ambulance drone for medical surveillance. J Phys Conf Ser 2022;2318:012023. IOP Publishing.
• [25] Ahmed MF, Mohanta JC, Zafar MN. Development of smart quadcopter for autonomous overhead power transmission line inspections. Mater Today Proceed 2022;51:261268.
• [26] Türkseven S, Kizmaz MZ, Tekin AB, Urkan E, Serim AT. Tarımda dijital dönüşüm; insansız hava araçları kullanımı. Tarım Makinaları Bilimi Dergisi 2016;12:267271.
• [27] Düzyol K, Budak S, Kurnaz E, Durdu A. Autonomous fire fighting mission using unmanned aerial vehicle image processing. J New Results Eng Nat Sci 2023;18:110.
• [28] Kinaneva D, Hristov G, Raychev J and Zahariev P. Early Forest Fire Detection Using Drones and Artificial Intelligence. 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2019, pp. 10601065.
• [29] Roldán‐Gómez JJ, González-Gironda E, & Barrientos A. A survey on Robotic Technologies for forest Firefighting: Applying drone swarms to improve firefighters’ efficiency and safety. Appl Sci 2021;11:363.
• [30] Kara M, Yumuşak R, Eren T. Anız Yangınlarına Müdahale için İtfaiye Drone Seçimi: Giresun Örneği. J Aviat Res 2023;5:115.
• [31] Lohit GVS, Reforestation Using Drones and Deep Learning Techniques, 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2021. p. 847852.
• [32] Lohit GVS and Bisht D, Seed Dispenser using Drones and Deep Learning Techniques for Reforestation, 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2021. p. 12751283.
• [33] Benic Z, Piljek P, Kotarski D. Mathematical modelling of unmanned aerial vehicles with four rotors, Interdiscip Descr Complex Syst 2016;14:88–100.
• [34] SunnySky X2216 Brushless Motors Short Shaft Version. (n.d.). SunnySky USA. Retrieved November 3, 2023, https://sunnyskyusa.com/products/sunnysky-x2216-brushless-motors
• [35] Ulutaş T, Avci O, Akar E, Köksal B, Kalkan Y, Simple Design and Implementation of Two-Way Communication System through UAV. Balkan J Electr Comput Eng 2023;11:61–70.