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Effectiveness Analysis of Different Spray Route Models in Agricultural Spraying

Year 2025, Volume: 10 Issue: 1, 19 - 32, 01.06.2025

Abstract

This study examines the efficiency and energy performance of different spray routes in agricultural spraying operations using unmanned aerial vehicles (UAVs). Specifically, the performance of straight-line, zigzag, and spiral methods was compared in terms of energy consumption, spraying duration, and effectiveness rates. Experiments were conducted on a 1-hectare agricultural area under constant environmental conditions. The findings indicate that the straight-line method provided the lowest energy consumption (15% battery) and the fastest spraying duration (20 minutes) but was limited in achieving uniform coverage in complex areas. The zigzag method demonstrated higher uniformity (85%) and effectiveness, making it more suitable for complex fields. The spiral method achieved the highest uniformity and spraying effectiveness (88%) but resulted in the highest energy consumption (20% battery). As drone speed increased, spraying duration decreased, while efficiency and uniformity peaked at a speed of 5 m/s. The study emphasizes that the choice of spraying methods should depend on field geometry, crop type, and operational priorities. These findings provide valuable guidance for optimizing drone-based agricultural spraying systems in terms of energy efficiency and sustainability. Continuity in such research and the integration of the results into agricultural practices are crucial for enhancing agricultural productivity and supporting sustainable farming practices

References

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  • Guo, H., Zhou, J., Liu, F., He, Y., Huang, H., & Wang, H. (2020). Application of machine learning methods to quantitatively evaluate the droplet size and deposition distribution of the UAV spray nozzle. Applied Sciences, 10(5), 1759. https://doi.org/10.3390/app10051759
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  • Li, J. (2023). Coverage path planning method for agricultural spraying UAV in arbitrary polygon area. Aerospace, 10(9), 755. https://doi.org/10.3390/aerospace10090755
  • Li, X., Giles, D., Niederholzer, F., Andaloro, J., Lang, E., & Watson, L. (2020). Evaluation of an unmanned aerial vehicle as a new method of pesticide application for almond crop protection. Pest Management Science, 77(1), 527–537. https://doi.org/10.1002/ps.6052
  • Li, Z., & Wang, Y. (2018). Research on the application of UAV in agricultural spraying. Journal of Agricultural Science and Technology, 20(2), 123–134.
  • Lou, Z., Fang, X., Han, X., Lan, Y., Duan, T., & Wei, F. (2018). Effect of unmanned aerial vehicle flight height on droplet distribution, drift, and control of cotton aphids and spider mites. Agronomy, 8(9), 187. https://doi.org/10.3390/agronomy8090187
  • Meng, Y., Su, J., Song, J., Chen, W., & Lan, Y. (2020). Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution. Computers and Electronics in Agriculture, 170, 105282. https://doi.org/10.1016/j.compag.2020.105282
  • Ming, R. (2023). Comparative analysis of different UAV swarm control methods on unmanned farms. Agronomy, 13(10), 2499. https://doi.org/10.3390/agronomy13102499
  • Morales-Rodríguez, P., Cano, E., Villena, J., & López-Perales, J. (2022). A comparison between conventional sprayers and new UAV sprayers: A study case of vineyards and olives in Extremadura (Spain). Agronomy, 12(6), 1307. https://doi.org/10.3390/agronomy12061307
  • Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Remote Sensing, 5(2), 1001–1019. https://doi.org/10.3390/rs5021001
  • Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 72(4), 411–458. https://doi.org/10.1002/net.21818
  • ÖZYURT, H., Duran, H., & Çelen, İ. (2022). Determination of the application parameters of spraying drones for crop production in hazelnut orchards. Tekirdağ Ziraat Fakültesi Dergisi, 19(4), 819–828. https://doi.org/10.33462/jotaf.1105420
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  • Poikonen, S., & Campbell, J. (2020). Future directions in drone routing research. Networks, 77(1), 116–126. https://doi.org/10.1002/net.21982
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Tarımsal İlaçlamada Farklı Püskürtme Rotası Modellerinin Etkinlik Analizi

Year 2025, Volume: 10 Issue: 1, 19 - 32, 01.06.2025

Abstract

Bu çalışma, tarımsal ilaçlama operasyonlarında insansız hava araçları (İHA) kullanılarak farklı püskürtme rotalarının etkinliğini ve enerji verimliliğini incelemektedir. Özellikle düz çizgi, zikzak ve spiral yöntemlerin performansı, enerji tüketimi, ilaçlama süresi ve etkinlik oranları açısından karşılaştırılmıştır. Deneyler, 1 hektarlık bir tarım alanında, sabit çevresel koşullar altında gerçekleştirilmiştir. Bulgular, düz çizgi yönteminin en düşük enerji tüketimi (%15 batarya) ve en hızlı ilaçlama süresini (20 dakika) sağladığını, ancak karmaşık alanlarda homojenlik açısından sınırlı kaldığını göstermektedir. Zikzak yöntemi, daha yüksek homojenlik (%85) ve etkinlik sunarak karmaşık alanlarda daha etkili olmuştur. Spiral yöntem ise en yüksek homojenlik ve ilaçlama etkinliğini (%88) sağlamış, ancak en fazla enerji tüketimine (%20 batarya) yol açmıştır. Drone hızının artışıyla ilaçlama süresi azalırken, verimlilik ve homojenlik 5 m/s hızında en yüksek düzeye ulaşmıştır. Çalışma, farklı püskürtme yöntemlerinin arazi geometrisi, mahsul tipi ve operasyonel önceliklere bağlı olarak seçilmesi gerektiğini vurgulamaktadır. Bu bulgular, drone tabanlı tarımsal ilaçlama sistemlerinin enerji verimliliği ve sürdürülebilirlik açısından optimize edilmesine yönelik önemli bir rehber sunmaktadır. Tarımsal verimliliğin artırılması ve sürdürülebilir tarım uygulamalarının desteklenmesi için bu tür araştırmaların sürekliliği ve elde edilen sonuçların tarımsal uygulamalara entegre edilmesi büyük bir önem arz etmektedir

References

  • Arafat, M. Y., & Moh, S. (2022). JRCS: Joint routing and charging strategy for logistics drones. IEEE Internet of Things Journal, 9(21), 21751–21764. https://doi.org/10.1109/jiot.2022.3182750
  • Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146. https://doi.org/10.1890/120150
  • Arakawa, T., & Kamio, S. (2023). Control efficacy of UAV-based ultra-low-volume application of pesticide in chestnut orchards. Plants, 12(14), 2597. https://doi.org/10.3390/plants12142597
  • Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L., & Glielmo, L. (2022). A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture. Journal of Navigation, 75(2), 364–383. https://doi.org/10.1017/s0373463321000825
  • Cavalaris, C. (2023). Cost analysis of using UAV sprayers for olive fruit fly control. Agriengineering, 5(4), 1925–1942. https://doi.org/10.3390/agriengineering5040118
  • Chen, C., Huang, Y., Li, Y., Chen, Y., Chang, C., & Huang, Y. (2021). Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986–21997. https://doi.org/10.1109/access.2021.3056082
  • Chen, S., Lan, Y., Zhou, Z., Deng, X., & Wang, J. (2021). Research advances of the drift-reducing technologies in application of agricultural aviation spraying. International Journal of Agricultural and Biological Engineering, 14(5), 1–10. https://doi.org/10.25165/j.ijabe.20211405.6225
  • Chen, Y., Hou, C., Tang, Y., Zhuang, J., Lin, J., & Luo, S. (2019). An effective spray drift-reducing method for a plant-protection unmanned aerial vehicle. International Journal of Agricultural and Biological Engineering, 12(5), 14–20. https://doi.org/10.25165/j.ijabe.20191205.4289
  • Chojnacki, J., & Pachuta, A. (2021). Impact of the parameters of spraying with a small unmanned aerial vehicle on the distribution of liquid on young cherry trees. Agriculture, 11(11), 1094. https://doi.org/10.3390/agriculture11111094
  • Deng, Qian & Zhang, Yuhan & Lin, Zhuyu & Gao, Xueping & Weng, Zhenlin. (2024). The Impact of Digital Technology Application on Agricultural Low-Carbon Transformation—A Case Study of the Pesticide Reduction Effect of Plant Protection Unmanned Aerial Vehicles (UAVs). Sustainability. 16. 10920. https://doi.org/10.3390/su162410920
  • Dükkancı, O., Kara, B., & Bektaş, T. (2021). Minimizing energy and cost in range-limited drone deliveries with speed optimization. Transportation Research Part C Emerging Technologies, 125, 102985. https://doi.org/10.1016/j.trc.2021.102985
  • FAN, L. (2023). Agricultural UAV crop spraying path planning based on PSO-A* algorithm. INMATEH Agricultural Engineering, 625–636. https://doi.org/10.35633/inmateh-71-54
  • Filho, F., Heldens, W., Kong, Z., & Lange, E. (2019). Drones: Innovative technology for use in precision pest management. Journal of Economic Entomology, 113(1), 1–25. https://doi.org/10.1093/jee/toz268
  • Guo, H., Zhou, J., Liu, F., He, Y., Huang, H., & Wang, H. (2020). Application of machine learning methods to quantitatively evaluate the droplet size and deposition distribution of the UAV spray nozzle. Applied Sciences, 10(5), 1759. https://doi.org/10.3390/app10051759
  • Ivezić, A. (2023). Drone-related agrotechnologies for precise plant protection in Western Balkans: Applications, possibilities, and legal framework limitations. Agronomy, 13(10), 2615. https://doi.org/10.3390/agronomy13102615
  • İnan, M., & Karci, A. (2021). Tarımda Ağaç İlaçlamanın Drone’larla Yapılmasında Yeni bir Yöntemin Geliştirilmesi ve Uygulanması. Computer Science, 6(2), 72-89.
  • Just, G. E., Pellenz, M. E., Lima, L. A. P. A. d., Chang, B. S., Souza, R. D., & Montejo‐Sánchez, S. (2020). UAV path optimization for precision agriculture wireless sensor networks. Sensors, 20(21), 6098. https://doi.org/10.3390/s20216098
  • Karkee, M., & Zhang, Q. (2015). Performance evaluation of a UAV for agricultural applications. Transactions of the ASABE, 58(2), 469–477. https://doi.org/10.13031/trans.58.10977
  • Kovalev, I. (2023). Analysis of system parameters in a microprocessor performance model of a swarm of agricultural spraying UAVs. IOP Conference Series Earth and Environmental Science, 1284(1), 012030. https://doi.org/10.1088/1755-1315/1284/1/012030
  • Kovalev, I. (2023). Performance analysis of UAV sprayer application in precision agriculture. IOP Conference Series Earth and Environmental Science, 1231(1), 012057. https://doi.org/10.1088/1755-1315/1231/1/012057
  • Liu, Z., Gao, R., Zhao, Y., Wu, H., Liang, Y., Liang, K., Liu, D., Huang, T., Xie, S., Lv, J., & Li, J. (2024). Study on the characteristics of downwash field range and consistency of spray deposition of agricultural UAVs. Agriculture, 14(6), 931. https://doi.org/10.3390/agriculture14060931
  • Li, J. (2023). Coverage path planning method for agricultural spraying UAV in arbitrary polygon area. Aerospace, 10(9), 755. https://doi.org/10.3390/aerospace10090755
  • Li, X., Giles, D., Niederholzer, F., Andaloro, J., Lang, E., & Watson, L. (2020). Evaluation of an unmanned aerial vehicle as a new method of pesticide application for almond crop protection. Pest Management Science, 77(1), 527–537. https://doi.org/10.1002/ps.6052
  • Li, Z., & Wang, Y. (2018). Research on the application of UAV in agricultural spraying. Journal of Agricultural Science and Technology, 20(2), 123–134.
  • Lou, Z., Fang, X., Han, X., Lan, Y., Duan, T., & Wei, F. (2018). Effect of unmanned aerial vehicle flight height on droplet distribution, drift, and control of cotton aphids and spider mites. Agronomy, 8(9), 187. https://doi.org/10.3390/agronomy8090187
  • Meng, Y., Su, J., Song, J., Chen, W., & Lan, Y. (2020). Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution. Computers and Electronics in Agriculture, 170, 105282. https://doi.org/10.1016/j.compag.2020.105282
  • Ming, R. (2023). Comparative analysis of different UAV swarm control methods on unmanned farms. Agronomy, 13(10), 2499. https://doi.org/10.3390/agronomy13102499
  • Morales-Rodríguez, P., Cano, E., Villena, J., & López-Perales, J. (2022). A comparison between conventional sprayers and new UAV sprayers: A study case of vineyards and olives in Extremadura (Spain). Agronomy, 12(6), 1307. https://doi.org/10.3390/agronomy12061307
  • Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Remote Sensing, 5(2), 1001–1019. https://doi.org/10.3390/rs5021001
  • Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 72(4), 411–458. https://doi.org/10.1002/net.21818
  • ÖZYURT, H., Duran, H., & Çelen, İ. (2022). Determination of the application parameters of spraying drones for crop production in hazelnut orchards. Tekirdağ Ziraat Fakültesi Dergisi, 19(4), 819–828. https://doi.org/10.33462/jotaf.1105420
  • Padhye, A., Anthoulakis, E., Christodoulou, S., Zervas, M., Konstantaki, M., & Pissadakis, S. (2022). Optical fiber sensors for detecting spraying drift in drone agricultural applications. Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII. https://doi.org/10.1117/12.2609294
  • Poikonen, S., & Campbell, J. (2020). Future directions in drone routing research. Networks, 77(1), 116–126. https://doi.org/10.1002/net.21982
  • Rani, R. (2022). Pineapple crop fertilizer application method: A preliminary study on the performance and effectiveness of a boom sprayer and a sprayer drone. Advances in Agricultural and Food Research Journal, 4(1). https://doi.org/10.36877/aafrj.a0000349
  • Sánchez-Fernández, L. (2024). Reducing environmental exposure to PPPs in super-high-density olive orchards using UAV sprayers. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1272372
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There are 51 citations in total.

Details

Primary Language Turkish
Subjects Energy-Efficient Computing, Computer Software
Journal Section Research Article
Authors

Mevlüt İnan 0000-0002-9840-8404

Ali Karci 0000-0002-8489-8617

Publication Date June 1, 2025
Submission Date January 10, 2025
Acceptance Date February 26, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA İnan, M., & Karci, A. (2025). Tarımsal İlaçlamada Farklı Püskürtme Rotası Modellerinin Etkinlik Analizi. Computer Science, 10(1), 19-32. https://doi.org/10.53070/bbd.1616740

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