Environmental-Aware Intelligent Precision Agriculture: Fuzzy Logic and AI-Based Adaptive Herbicide Spraying Strategy for Ragweed

Öz

The widespread presence of ragweed (Ambrosia artemisiifolia) poses a significant threat to agricultural productivity and biodiversity across multiple cropping systems. Traditional herbicide application methods often result in overuse, environmental contamination, and ineffective weed suppression, especially under variable environmental conditions such as fluctuating temperature, humidity, and wind speed. These conventional approaches apply uniform spray rates regardless of individual plant characteristics or local environmental factors, leading to substantial chemical waste and increased environmental risks. In this work, we propose an intelligent precision spraying control system that integrates a novel fuzzy logic controller to dynamically adjust herbicide application in real-time based on multiple input parameters. Unlike existing studies that rely on uniform broadcast rates or simple threshold-based activation, the proposed system performs per-plant adaptive mixing and dosage control, representing a novel decision layer in precision weed management. The fuzzy logic controller processes seven input parameters, including plant height, leaf area index, temperature, humidity, wind speed, water pH, and resistance risk, and generates optimized outputs for water amount, herbicide selection, surfactant type, adjuvant selection, and drift control agent. Simulation results using ten synthetic test cases representing diverse field conditions demonstrated an approximately 15% reduction in per-plant spray volume, and potential total-use reductions of 25–30% when combined with selective spot-spray activation compared with the conventional 100-200 L ha⁻¹ broadcast baseline derived from field guidelines and extension recommendations. The controller successfully adapted spray composition to varying plant sizes and environmental conditions, with taller plants and higher leaf area index receiving proportionally higher dosages while maintaining efficacy. These findings highlight the potential of adaptive fuzzy control to minimize chemical input, reduce spray drift, and improve environmental sustainability in precision agriculture systems. The proposed controller is scalable for autonomous UAVs and robotic agricultural platforms, making it a viable solution for precision ragweed management in modern farming operations.

Anahtar Kelimeler

• Intelligent agricultural robotics
• Precision agriculture
• Adaptive decision systems
• Environmental-aware spraying
• Herbicide control
• Smart agriculture

Çevresel Farkındalıklı Akıllı Hassas Tarım: Pelin Otu İçin Bulanık Mantık ve Yapay Zekâ Tabanlı Uyarlanabilir Herbisit Püskürtme Stratejisi

Öz

Ambrosia artemisiifolia (yabani pelin) türünün yaygın varlığı, tarımsal verimlilik ve biyolojik çeşitlilik için önemli bir tehdit oluşturmaktadır. Geleneksel herbisit uygulama yöntemleri genellikle aşırı kimyasal kullanımına, çevresel kirliliğe ve özellikle değişken çevre koşulları altında etkisiz yabancı ot bastırmasına yol açmaktadır. Bu çalışmada, herbisit uygulamasını gerçek zamanlı olarak dinamik biçimde ayarlayan yenilikçi bir bulanık mantık denetleyicisini entegre eden akıllı bir hassas püskürtme sistemi kontrolörü önerilmektedir. Mevcut çalışmalarda kullanılan sabit yayma oranlarının aksine, önerilen sistem bitki bazında uyarlanabilir karışım ve doz kontrolü gerçekleştirmekte olup, hassas yabancı ot yönetiminde yeni bir karar katmanı temsil etmektedir. Simülasyon sonuçları, önerilen sistemin, tarla kılavuzlarından alınan 100–200 L/ha’lık geleneksel yayma uygulamasına kıyasla toplam herbisit kullanımını ortalama %30 oranında azalttığını göstermiştir. Bu bulgular, uyarlanabilir bulanık denetimin kimyasal girdiyi en aza indirme, püskürtme sürüklenmesini azaltma ve çevresel sürdürülebilirliği geliştirme potansiyelini vurgulamaktadır. Önerilen denetleyici, otonom İHA’lar ve tarımsal robotik platformlara ölçeklenebilir niteliktedir ve modern tarımda hassas yabani ot yönetimi için uygulanabilir bir çözüm sunmaktadır.

Anahtar Kelimeler

• Akıllı tarımsal robotik sistemler
• Hassas tarım
• Uyarlamalı karar verme sistemleri
• Çevresel koşullara duyarlı püskürtme
• Herbisit kontrolü
• Akıllı tarım

Kaynakça

1. Allmendinger, A., Spaeth, M., Saile, M., Peteinatos, G. G. and Gerhards, R. (2024). Agronomic and technical evaluation of herbicide spot spraying in maize based on high-resolution aerial weed maps-An on-farm trial. Plants, 13(15): 2164. https://doi.org/10.3390/plants13152164
2. Beale, B. and Morris, M. (2019). Managing herbicide-resistant common ragweed (FS-474). University of Maryland Extension, College Park, MD, USA. https://extension.umd.edu/sites/extension.umd.edu/files/publications/ManagingHerbicideResistantCommonRagweed_FS-474_ada.pdf
3. Beam, S. C., Cahoon, C. W., Haak, D. C., Holshouser, D. L., Mirsky, S. B. and Flessner, M. L. (2021). Integrated weed management systems to control common ragweed (Ambrosia artemisiifolia L.) in soybean. Frontiers in Agronomy, 2, 598426. https://doi.org/10.3389/fagro.2020.598426
4. Candan, F., Dik, O. F., Kumbasar, T., Mahfouf, M. and Mihaylova, L. (2023). Real-time interval type-2 fuzzy control of an unmanned aerial vehicle with flexible cable-connected payload. Algorithms, 16(6): 273. https://doi.org/10.3390/a16060273
5. D’Amico, F. Jr., Besançon, T., Koehler, A., Shergill, L., Ziegler, M. and VanGessel, M. (2024). Common ragweed (Ambrosia artemisiifolia L.) accessions in the Mid-Atlantic region resistant to ALS-, PPO-, and EPSPS-inhibiting herbicides. Weed Technology, 38, e30. https://doi.org/10.1017/wet.2024.11
6. Fabula, J., Sharda, A., Kang, Q. and Flippo, D. (2021). Nozzle flow rate, pressure drop, and response time of pulse width modulation (PWM) nozzle control systems. Transactions of the ASABE, 64(5): 1519–1532. https://doi.org/10.13031/trans.14360
7. Gerhards, R. and Christensen, S. (2003). Real-time weed detection, decision making and patch spraying in maize, sugar beet, winter wheat and winter barley. Weed Research, 43(6): 385–392. https://doi.org/10.1046/j.1365-3180.2003.00349.x
8. Getahun, S., Kefale, H. and Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024(1): 2126734. https://doi.org/10.1155/2024/2126734

9. Hansen, P. K., Holst, N., Kudsk, P., Mathiassen, S. K., Simončič, A., Lešnik, M., Bohren, C., Waldispühl, S., Verschwele, A., Wassmuth, B., Starfinger, U., Buttenschøn, R. and Ravn, H. P. (Holst, N., Ed.). (2009). Strategies for Ambrosia control (AMBROSIA 2008-2009): Scientific report. Euphresco Project, Aarhus University, Denmark. https://www.euphresco.net/media/project_reports/ambrosia_report.pdf
10. Iamonico, D. (2016). Ambrosia artemisiifolia (common ragweed). CABI Compendium. https://doi.org/10.1079/cabicompendium.4691
11. Jeon, H. Y., Tian, L. F. and Zhu, H. (2011). Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors, 11(6): 6270–6283. https://doi.org/10.3390/s110606270
12. Kara, A. and Ata, E. (2021). Weed species, density and frequency of occurrence in vineyard areas of Tekirdağ Province, Turkey. Journal of Tekirdag Agricultural Faculty, 18(2): 333–343. https://doi.org/10.33462/jotaf.799731
13. Kaur, S., Mendes, K. F., Ekeleme, F., Chachalis, D. and Chauhan, B. S. (2024). Editorial: Herbicide efficacy. Frontiers in Agronomy, 6, 1504465. https://doi.org/10.3389/fagro.2024.1504465
14. Knolmajer, B., Jócsák, I., Taller, J., Keszthelyi, S. and Kazinczi, G. (2025). Common ragweed—Ambrosia artemisiifolia L.: A review with special regards to the latest results in protection methods, herbicide resistance, new tools and methods. Agronomy, 15(8): 1765. https://doi.org/10.3390/agronomy15081765
15. Kunz, C., Weber, J. F., Peteinatos, G. G., Sökefeld, M. and Gerhards, R. (2018). Camera-steered mechanical weed control in sugar beet, maize and soybean. Precision Agriculture, 19: 708–720. https://doi.org/10.1007/s11119-017-9551-4
16. Meena, R., Virk, S., Rains, G. and Porter, W. (2024). Comparative performance of a sprayer rate controller and pulse width modulation (PWM) systems for site-specific pesticide applications. AgriEngineering, 6(3): 3312–3326. https://doi.org/10.3390/agriengineering6030189
17. Messelhäuser, M. H., Saile, M., Sievernich, B. and Gerhards, R. (2021). Effect of cinmethylin against Alopecurus myosuroides Huds. in winter cereals. Plant, Soil and Environment, 67(1): 46-54. https://doi.org/10.17221/586/2020-PSE
18. Mohammadi, V., Sodjinou, S. G., Katakpe, K. K., Rossé, M. and Gouton, P. (2022). Development of a multi-spectral camera for computer vision applications. In Proceedings of the 30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2022), Pilsen, Czech Republic. https://doi.org/10.24132/CSRN.3201.4
19. Sanci, M. E., Narwal, R. and Li, L. (2025). Targeted weed control: AI-driven spot spraying system with environmental adaptation. In Proceedings of the 10th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE) (pp. 1–5). Stara Zagora, Bulgaria. https://doi.org/10.1109/EEAE65901.2025.11273560
20. Sayın, B., Bayav, A., Besen, T., Emre, M., Celikyurt, M. A., Karamursel, D., Kuzgun, M., Yilmaz, S. G. and Arslan, S. (2021). Farmers' opinions on biological and biotechnical pest control support programs in Turkey. Journal of TekirdagAgricultural Faculty, 18(2): 344–358. https://doi.org/10.33462/jotaf.800087
21. Søgaard, H. T. (2005). Weed classification by active shape models. Biosystems Engineering, 91(3): 271–281. https://doi.org/10.1016/j.biosystemseng.2005.04.011
22. Spaeth, M., Sökefeld, M., Schwaderer, P., Gauer, M. E., Sturm, D. J., Delatrée, C. C. and Gerhards, R. (2024). Smart sprayer: A technology for site-specific herbicide application. Crop Protection, 177, 106564. https://doi.org/10.1016/j.cropro.2023.106564
23. Villette, S., Maillot, T., Guillemin, J. P. and Douzals, J. P. (2021). Simulation-aided study of herbicide patch spraying: Influence of spraying features and weed spatial distributions. Computers and Electronics in Agriculture, 182: 105981. https://doi.org/10.1016/j.compag.2020.105981