TR
EN
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
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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
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