Research Article
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Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis

Year 2025, Volume: 8 Issue: 4, 991 - 998, 15.07.2025
https://doi.org/10.34248/bsengineering.1661866

Abstract

This article discusses the importance of spraying in precision agriculture to optimize land use, particularly in response to increasing population and declining agricultural land. A six-rotor unmanned aerial vehicle (UAV) was designed to maximize spraying efficiency and minimize waste. The required pesticide amount was determined based on the number of trees in the field, and UAV components capable of autonomous spraying were selected accordingly. Autonomous flight tests were conducted using a color-based object detection algorithm for tree identification. Success rates are calculated by the ratio of color-changing areas in images captured by the thermal camera to the total area. The results indicate that in low-wind conditions, the spraying success rate can reach 92%, whereas in high-wind conditions, it drops to 20%. Comparisons with traditional spraying methods reveal that tractor-based spraying achieves the same efficiency (92%) but requires 1.5 times longer spraying time and twice the pesticide amount. In contrast, hand-pump spraying reaches 97% efficiency but requires 7.5 times longer and consumes 3.5 times more pesticide. In addition, when comparing spraying to be done on large agricultural lands such as 10 acres, in addition to the amount of spraying and water, diesel fuel is added for spraying with a tractor, personnel costs are added for spraying by hand, while only the electricity cost to charge the battery is added for spraying with a UAV. The effect of wind speed on the success rate can be ensured by revising the UAV position after the calculations are made after the wind direction and speed are determined, and stability can be ensured in future studies.

Project Number

KBÜBAP-23-YL-090

References

  • Abdellatif M. 2008. Effect of color pre-processing on color-based object detection, 2008 SICE Annual Conference, Chofu, Japan, pp: 1124-1129. https://doi.org/10.1109/SICE.2008.4654827.
  • Alsalam BHY, Morton K, Campbell D, Gonzalez F. 2017. Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture, IEEE Aerospace Conference, Big Sky, MT, USA, pp: 1-12. https://doi.org/10.1109/AERO.2017.7943593.
  • Anonymous. 2024. URL: https://www.toros.com.tr/wp-content/uploads/2024/05/dosya_tarimda-verimlilik.pdf (accessed date: April 15, 2025).
  • Anonymous. 2025. InRange function and color detection. OpenCV Documentation. https://docs.opencv.org/4.x/da/d97/tutorial_threshold_inRange.html (accessed date: February 18, 2025).
  • Arbat G, Masseroni D. 2024. The use and management of agricultural irrigation systems and technologies. Agriculture, 14: 236. pp: 236.
  • Arsov T, Kiprijanovski M, Gjamovski V, Saraginovski N. 2019. Performance of some cherry cultivars growing on different planting distances, IV Balkan Symp Fruit Growing, pp: 119-124.
  • Avşar E, Yalçın M, Boran N, Ay Z. 2021. Döner Kanatlı İnsansız Hava Aracı Tasarımı, Karadeniz Teknik Üniversitesi, Mühendislik Fakültesi Makine Mühendisliği Bölümü, Trabzon, Türkiye, pp: 48.
  • Azman N, Wahyudin L, Fathoni M. 2021. Design and testing of an autonomous mode quadrotor with Fixhawk PX4 for real-time video monitoring. Int J Sci Technol Res, 10(3): 2277-8616. pp: 2277-8616.
  • Çaylı A, Akyüz A, Baytorun AN, Üstün S, Boyacı S. 2016. Seralarda ısı kaybına neden olan yapısal sorunların termal kamera ile belirlenmesi. KSÜ Doğa Bilimleri Derg, 19(1): 5-14. https://doi.org/10.18016/ksujns.36715. pp: 5-14.
  • Diwan T, Anirudh G, Tembhurne JV. 2023. Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed Tools Appl, 82: 9243-9275. https://doi.org/10.1007/s11042-022-13644-y. pp: 9243-9275.
  • Gunturu R, Durgaa KN, Harshaa TS, Ahamed SF. 2020. Development of drone based delivery system using Pixhawk flight controller, Proc. 2nd Int. Conf. IoT, Social, Mobile, Analytics & Cloud Comput Vision Bio-Eng (ISMAC-CVB), November 2020. https://doi.org/10.2139/ssrn.3734801. pp: 1-7.
  • Herlambang L, Kuncoro E, Huda MM. 2021. The implementation of autonomous waypoint in reconnaissance plane (unmanned aerial vehicle) UAV GALAK-24 use with mission planner. Pustaka Poltekad, J Telkommil, 2. http://journal.poltekad.ac.id/index.php/kom/article/view/183/138. pp: 1-6.
  • Hole F. 1984. A reassessment of the Neolithic revolution. Paléorient, 10(2): 49-60. pp: 49-60.
  • Iglesias A, Rosenzweig C, Pereira D. 2000. Agricultural impacts of climate change in Spain: developing tools for a spatial analysis. Glob Environ Change, 10(1): 69-80. pp: 69-80.
  • Jacquet F, Jeuffroy MH, Jouan J. 2022. Pesticide-free agriculture as a new paradigm for research. Agron Sustain Dev, 42: 8. pp: 8.
  • Karásek R, Kallies C. 2024. High-level mission planning for multi-agent indoor system, Proc. 2024 Integrated Commun., Navigat Surveill Conf (ICNS), pp: 1-7.
  • Kaur R, Choudhary D, Bali S, Bandral SS, Singh V, Ahmad MA, Rani N, Singh TG, Chandrasekaran B. 2024. Pesticides: An alarming detrimental to health and environment. Sci Total Environ, 915: 170113. https://doi.org/10.1016/j.scitotenv.2024.170113. pp: 170113.
  • Pimentel D. 1996. Green revolution agriculture and chemical hazards. Sci Total Environ, 188(Supplement): 86-98. pp: 86-98.
  • Sir Attenborough D. 2024. URL: https://populationmatters.org (accessed date: October 24, 2023).
  • Stamate MA, Nicolescu AF, Pupază C. 2017. Mathematical model of a multi-rotor drone prototype and calculation algorithm for motor selection. Proc Manuf Syst, 12(3): 119-128. pp: 119-128.
  • Tran QH, Han D, Kang C, Haldar A, Huh J. 2017. Effects of ambient temperature and relative humidity on subsurface defect detection in concrete structures by active thermal imaging. Sensors, 17: 1718. https://doi.org/10.3390/s17081718. pp: 1718.
  • Tudi M, Ruan DH, Wang L, Lyu J, Sadler R, Connell D, Chu C, Phung DT. 2021. Agriculture development, pesticide application and its impact on the environment. Int J Environ Res Public Health, 18: 1112. pp: 1112.
  • Velusamy P, Rajendran S, Mahendran RK, Naseer S, Shafiq M, Choi JG. 2022. Unmanned aerial vehicles (UAV) in precision agriculture: applications and challenges. Energies, 15(1): 217. https://doi.org/10.3390/en15010217. pp: 217.
  • Yalçıner CÇ, Gündoğdu E, Kurban YC, Altunel E. 2017. Eski eserlerdeki yapısal tahribatların termal görüntüleme ve mikrodalga nem ölçümleri ile belirlenmesi: Ayasofya Müzesi örnek çalışması. ÇOMÜ Fen Bilim Enst Derg, 3(2): 34-47. https://doi.org/10.28979/comufbed.346240.

Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis

Year 2025, Volume: 8 Issue: 4, 991 - 998, 15.07.2025
https://doi.org/10.34248/bsengineering.1661866

Abstract

This article discusses the importance of spraying in precision agriculture to optimize land use, particularly in response to increasing population and declining agricultural land. A six-rotor unmanned aerial vehicle (UAV) was designed to maximize spraying efficiency and minimize waste. The required pesticide amount was determined based on the number of trees in the field, and UAV components capable of autonomous spraying were selected accordingly. Autonomous flight tests were conducted using a color-based object detection algorithm for tree identification. Success rates are calculated by the ratio of color-changing areas in images captured by the thermal camera to the total area. The results indicate that in low-wind conditions, the spraying success rate can reach 92%, whereas in high-wind conditions, it drops to 20%. Comparisons with traditional spraying methods reveal that tractor-based spraying achieves the same efficiency (92%) but requires 1.5 times longer spraying time and twice the pesticide amount. In contrast, hand-pump spraying reaches 97% efficiency but requires 7.5 times longer and consumes 3.5 times more pesticide. In addition, when comparing spraying to be done on large agricultural lands such as 10 acres, in addition to the amount of spraying and water, diesel fuel is added for spraying with a tractor, personnel costs are added for spraying by hand, while only the electricity cost to charge the battery is added for spraying with a UAV. The effect of wind speed on the success rate can be ensured by revising the UAV position after the calculations are made after the wind direction and speed are determined, and stability can be ensured in future studies.

Project Number

KBÜBAP-23-YL-090

References

  • Abdellatif M. 2008. Effect of color pre-processing on color-based object detection, 2008 SICE Annual Conference, Chofu, Japan, pp: 1124-1129. https://doi.org/10.1109/SICE.2008.4654827.
  • Alsalam BHY, Morton K, Campbell D, Gonzalez F. 2017. Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture, IEEE Aerospace Conference, Big Sky, MT, USA, pp: 1-12. https://doi.org/10.1109/AERO.2017.7943593.
  • Anonymous. 2024. URL: https://www.toros.com.tr/wp-content/uploads/2024/05/dosya_tarimda-verimlilik.pdf (accessed date: April 15, 2025).
  • Anonymous. 2025. InRange function and color detection. OpenCV Documentation. https://docs.opencv.org/4.x/da/d97/tutorial_threshold_inRange.html (accessed date: February 18, 2025).
  • Arbat G, Masseroni D. 2024. The use and management of agricultural irrigation systems and technologies. Agriculture, 14: 236. pp: 236.
  • Arsov T, Kiprijanovski M, Gjamovski V, Saraginovski N. 2019. Performance of some cherry cultivars growing on different planting distances, IV Balkan Symp Fruit Growing, pp: 119-124.
  • Avşar E, Yalçın M, Boran N, Ay Z. 2021. Döner Kanatlı İnsansız Hava Aracı Tasarımı, Karadeniz Teknik Üniversitesi, Mühendislik Fakültesi Makine Mühendisliği Bölümü, Trabzon, Türkiye, pp: 48.
  • Azman N, Wahyudin L, Fathoni M. 2021. Design and testing of an autonomous mode quadrotor with Fixhawk PX4 for real-time video monitoring. Int J Sci Technol Res, 10(3): 2277-8616. pp: 2277-8616.
  • Çaylı A, Akyüz A, Baytorun AN, Üstün S, Boyacı S. 2016. Seralarda ısı kaybına neden olan yapısal sorunların termal kamera ile belirlenmesi. KSÜ Doğa Bilimleri Derg, 19(1): 5-14. https://doi.org/10.18016/ksujns.36715. pp: 5-14.
  • Diwan T, Anirudh G, Tembhurne JV. 2023. Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed Tools Appl, 82: 9243-9275. https://doi.org/10.1007/s11042-022-13644-y. pp: 9243-9275.
  • Gunturu R, Durgaa KN, Harshaa TS, Ahamed SF. 2020. Development of drone based delivery system using Pixhawk flight controller, Proc. 2nd Int. Conf. IoT, Social, Mobile, Analytics & Cloud Comput Vision Bio-Eng (ISMAC-CVB), November 2020. https://doi.org/10.2139/ssrn.3734801. pp: 1-7.
  • Herlambang L, Kuncoro E, Huda MM. 2021. The implementation of autonomous waypoint in reconnaissance plane (unmanned aerial vehicle) UAV GALAK-24 use with mission planner. Pustaka Poltekad, J Telkommil, 2. http://journal.poltekad.ac.id/index.php/kom/article/view/183/138. pp: 1-6.
  • Hole F. 1984. A reassessment of the Neolithic revolution. Paléorient, 10(2): 49-60. pp: 49-60.
  • Iglesias A, Rosenzweig C, Pereira D. 2000. Agricultural impacts of climate change in Spain: developing tools for a spatial analysis. Glob Environ Change, 10(1): 69-80. pp: 69-80.
  • Jacquet F, Jeuffroy MH, Jouan J. 2022. Pesticide-free agriculture as a new paradigm for research. Agron Sustain Dev, 42: 8. pp: 8.
  • Karásek R, Kallies C. 2024. High-level mission planning for multi-agent indoor system, Proc. 2024 Integrated Commun., Navigat Surveill Conf (ICNS), pp: 1-7.
  • Kaur R, Choudhary D, Bali S, Bandral SS, Singh V, Ahmad MA, Rani N, Singh TG, Chandrasekaran B. 2024. Pesticides: An alarming detrimental to health and environment. Sci Total Environ, 915: 170113. https://doi.org/10.1016/j.scitotenv.2024.170113. pp: 170113.
  • Pimentel D. 1996. Green revolution agriculture and chemical hazards. Sci Total Environ, 188(Supplement): 86-98. pp: 86-98.
  • Sir Attenborough D. 2024. URL: https://populationmatters.org (accessed date: October 24, 2023).
  • Stamate MA, Nicolescu AF, Pupază C. 2017. Mathematical model of a multi-rotor drone prototype and calculation algorithm for motor selection. Proc Manuf Syst, 12(3): 119-128. pp: 119-128.
  • Tran QH, Han D, Kang C, Haldar A, Huh J. 2017. Effects of ambient temperature and relative humidity on subsurface defect detection in concrete structures by active thermal imaging. Sensors, 17: 1718. https://doi.org/10.3390/s17081718. pp: 1718.
  • Tudi M, Ruan DH, Wang L, Lyu J, Sadler R, Connell D, Chu C, Phung DT. 2021. Agriculture development, pesticide application and its impact on the environment. Int J Environ Res Public Health, 18: 1112. pp: 1112.
  • Velusamy P, Rajendran S, Mahendran RK, Naseer S, Shafiq M, Choi JG. 2022. Unmanned aerial vehicles (UAV) in precision agriculture: applications and challenges. Energies, 15(1): 217. https://doi.org/10.3390/en15010217. pp: 217.
  • Yalçıner CÇ, Gündoğdu E, Kurban YC, Altunel E. 2017. Eski eserlerdeki yapısal tahribatların termal görüntüleme ve mikrodalga nem ölçümleri ile belirlenmesi: Ayasofya Müzesi örnek çalışması. ÇOMÜ Fen Bilim Enst Derg, 3(2): 34-47. https://doi.org/10.28979/comufbed.346240.
There are 24 citations in total.

Details

Primary Language English
Subjects Precision Agriculture Technologies, Agricultural Machine Systems, Agricultural Machines
Journal Section Research Articles
Authors

Ahmet Faruk Tekin 0009-0000-6491-0079

Batıkan Erdem Demir 0000-0001-6400-1510

Project Number KBÜBAP-23-YL-090
Early Pub Date June 13, 2025
Publication Date July 15, 2025
Submission Date March 22, 2025
Acceptance Date May 8, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Tekin, A. F., & Demir, B. E. (2025). Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis. Black Sea Journal of Engineering and Science, 8(4), 991-998. https://doi.org/10.34248/bsengineering.1661866
AMA Tekin AF, Demir BE. Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis. BSJ Eng. Sci. July 2025;8(4):991-998. doi:10.34248/bsengineering.1661866
Chicago Tekin, Ahmet Faruk, and Batıkan Erdem Demir. “Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis”. Black Sea Journal of Engineering and Science 8, no. 4 (July 2025): 991-98. https://doi.org/10.34248/bsengineering.1661866.
EndNote Tekin AF, Demir BE (July 1, 2025) Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis. Black Sea Journal of Engineering and Science 8 4 991–998.
IEEE A. F. Tekin and B. E. Demir, “Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis”, BSJ Eng. Sci., vol. 8, no. 4, pp. 991–998, 2025, doi: 10.34248/bsengineering.1661866.
ISNAD Tekin, Ahmet Faruk - Demir, Batıkan Erdem. “Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis”. Black Sea Journal of Engineering and Science 8/4 (July 2025), 991-998. https://doi.org/10.34248/bsengineering.1661866.
JAMA Tekin AF, Demir BE. Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis. BSJ Eng. Sci. 2025;8:991–998.
MLA Tekin, Ahmet Faruk and Batıkan Erdem Demir. “Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis”. Black Sea Journal of Engineering and Science, vol. 8, no. 4, 2025, pp. 991-8, doi:10.34248/bsengineering.1661866.
Vancouver Tekin AF, Demir BE. Autonomous Agricultural Spraying UAV: Design, Implementation and Performance Analysis. BSJ Eng. Sci. 2025;8(4):991-8.

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