Araştırma Makalesi

A framework for AI-based plant disease detection and autonomous robotic agricultural spraying

Cilt: 17 Sayı: 1 19 Mart 2026
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A framework for AI-based plant disease detection and autonomous robotic agricultural spraying

Öz

This study presents a novel framework to detect plant diseases using artificial intelligence (AI) and efficient agricultural spraying using a mobile robot manipulator. The dataset for training the AI model was created by taking photos of plant leaves with and without disease and labeling the dataset according to the YOLO algorithm. A camera with a depth sensor providing point cloud data was used to detect plant disease and its location relative to the end effector was calculated using kinematic methods. The Robot Operating System (ROS) was used for system integration along with Moveit! package for kinematic calculations and motion planning of the robotic arm. The robotic arm is located on a two-wheeled mobile platform that autonomously navigates among the plants using Navigation-Stack of ROS. With the help of the developed spot spraying on the diseased area concept, not only the labor cost for agricultural spraying but also the amount of pesticides used for agricultural spraying can be reduced, lowering the pesticide costs and consumer exposure to the pesticides.

Anahtar Kelimeler

Kaynakça

  1. [1] Ghosh, “Mapped: The dramatic global rise of urbanization (1950-2020),” World Economic Forum, Feb. 9, 2019. [Online]. Available: https://www.weforum.org/agenda/2019/09/mapped-the-dramatic-global-rise-of-urbanization-1950-2020/
  2. [2] A. A. Akram, S. Chowdhury, and A. M. Mobarak, “Effects of emigration on rural labor markets,” National Bureau of Economic Research, Working Paper 23929, Oct. 2017. [Online]. Available: http://www.nber.org/papers/w23929
  3. [3] Bilgi Teknolojileri ve İletişim Kurumu (BTK), “Araştırma raporları,” 2019. [Online]. Available: https://btk.gov.tr/arastirma-raporlari. Accessed: Dec. 16, 2025.
  4. [4] European Commission, Farm to Fork Strategy: For a Fair, Healthy and Environmentally-Friendly Food System, COM(2020) 381 final, Brussels, Belgium, May 2020. [Online]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0381
  5. [5] A. Al Kuwaiti, K. Nazer, A. Al-Reedy, S. Al-Shehri, A. Al-Muhanna, A. V. Subbarayalu, D. Al Muhanna, and F. A. Al-Muhanna, “A review of the role of artificial intelligence in healthcare,” Journal of Personalized Medicine, vol. 13, no. 6, Art. no. 951, Jun. 2023. [Online]. Available: https://doi.org/10.3390/jpm13060951
  6. [6] C. Maraveas, “Incorporating artificial intelligence technology in smart greenhouses: Current state of the art,” Applied Sciences, vol. 13, no. 1, p. 14, 2022. [Online]. Available: https://doi.org/10.3390/app13010014
  7. [7] J. Pedersen and K. M. Lind, Precision Agriculture and the Future of Farming in Europe, European Parliament, 2017. [Online]. Available: https://www.europarl.europa.eu/RegData/etudes/STUD/2016/581892/EPRS_STU(2016)581892_EN.pdf
  8. [8] O. Karaçay and S. Kılıç, “The effects of agricultural tire technologies on soil compaction, traction performance and agricultural productivity,” Scientific Journal of Mehmet Akif Ersoy University, vol. 7, no. 2, pp. 64–80, 2024. [Online]. Available: https://doi.org/10.70030/sjmakeu.1563596

Ayrıntılar

Birincil Dil

İngilizce

Konular

Nöral Ağlar, Akıllı Robotik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

19 Mart 2026

Gönderilme Tarihi

30 Aralık 2024

Kabul Tarihi

16 Mart 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 17 Sayı: 1

Kaynak Göster

APA
Ök, B., & Işık, K. (2026). A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 17(1). https://doi.org/10.24012/dumf.1608369
AMA
1.Ök B, Işık K. A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. DÜMF MD. 2026;17(1). doi:10.24012/dumf.1608369
Chicago
Ök, Burhan, ve Kenan Işık. 2026. “A framework for AI-based plant disease detection and autonomous robotic agricultural spraying”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 17 (1). https://doi.org/10.24012/dumf.1608369.
EndNote
Ök B, Işık K (01 Mart 2026) A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 17 1
IEEE
[1]B. Ök ve K. Işık, “A framework for AI-based plant disease detection and autonomous robotic agricultural spraying”, DÜMF MD, c. 17, sy 1, Mar. 2026, doi: 10.24012/dumf.1608369.
ISNAD
Ök, Burhan - Işık, Kenan. “A framework for AI-based plant disease detection and autonomous robotic agricultural spraying”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 17/1 (01 Mart 2026). https://doi.org/10.24012/dumf.1608369.
JAMA
1.Ök B, Işık K. A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. DÜMF MD. 2026;17. doi:10.24012/dumf.1608369.
MLA
Ök, Burhan, ve Kenan Işık. “A framework for AI-based plant disease detection and autonomous robotic agricultural spraying”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, c. 17, sy 1, Mart 2026, doi:10.24012/dumf.1608369.
Vancouver
1.Burhan Ök, Kenan Işık. A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. DÜMF MD. 01 Mart 2026;17(1). doi:10.24012/dumf.1608369
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