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Journals / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2026 / Cilt: 17 Sayı: 1

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

Yapay-zeka tabanlı bitki hastalığı tespiti ve otonom robotik ilaçlama sistemi

Burhan Ök(KARABUK UNIVERSITY)0000-0003-3511-8482
Kenan Işık(KARABÜK ÜNİVERSİTESİ)0000-0002-0973-5180
Pages
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Publication Date
2026-03-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents a novel framework to detect plant diseases using artificial intelligence (AI) and efficient agricultural spraying using a mobile robot manipulator.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:tarımsal ilaçlama · yapay zeka · mabil manipülasyon

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.

Özet

Bu çalışma, yapay zeka (AI) kullanarak bitki hastalıklarını tespit etmek ve mobil bir robot manipülatörü kullanarak verimli tarımsal ilaçlama yapmak için yenilikçi bir çerçeve sunmaktadır. Yapay zeka modelinin eğitimi için veri seti, hastalıklı ve sağlıklı bitki yapraklarının fotoğraflarının çekilmesi ve veri setinin YOLO algoritmasına göre etiketlenmesiyle oluşturulmuştur. Bitki hastalığını tespit etmek için derinlik sensörüne sahip ve nokta bulutu verisi sağlayan bir kamera kullanılmış, hastalığın konumu kinematik yöntemlerle uç işlevciye göre hesaplanmıştır. Sistem entegrasyonu için Robot İşletim Sistemi (ROS) kullanılmış ve kinematik hesaplamalar ile robot kolunun hareket planlaması için Moveit! paketi entegre edilmiştir. Robot kolu, ROS'un Navigation-Stack paketi kullanılarak bitkiler arasında otonom olarak hareket eden iki tekerlekli bir mobil platform üzerine yerleştirilmiştir. Geliştirilen hastalıklı bölgeye noktasal ilaçlama konsepti sayesinde, tarımsal ilaçlama için gereken iş gücü maliyetlerinin yanı sıra kullanılan pestisit miktarı azaltılarak hem pestisit maliyetleri hem de tüketicilerin pestisitlere maruz kalma oranı düşürülebilmektedir.

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Cite This Article

Ök, B., Işık, K. (2026). A framework for AI-based plant disease detection and autonomous robotic agricultural spraying. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 17 Sayı: 1. https://doi.org/10.24012/dumf.1608369

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