Research Article

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

Volume: 17 Number: 1 March 19, 2026
TR EN

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

Abstract

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.

Keywords

References

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Details

Primary Language

English

Subjects

Neural Networks, Intelligent Robotics

Journal Section

Research Article

Publication Date

March 19, 2026

Submission Date

December 30, 2024

Acceptance Date

March 16, 2026

Published in Issue

Year 2026 Volume: 17 Number: 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. DUJE. 2026;17(1). doi:10.24012/dumf.1608369
Chicago
Ök, Burhan, and 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 (March 1, 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 and K. Işık, “A framework for AI-based plant disease detection and autonomous robotic agricultural spraying”, DUJE, vol. 17, no. 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 (March 1, 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. DUJE. 2026;17. doi:10.24012/dumf.1608369.
MLA
Ök, Burhan, and Kenan Işık. “A Framework for AI-Based Plant Disease Detection and Autonomous Robotic Agricultural Spraying”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 17, no. 1, Mar. 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. DUJE. 2026 Mar. 1;17(1). doi:10.24012/dumf.1608369