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Journals / Journal of Agricultural Sciences / 2026 / Volume: 32 Issue: 4

Design and Control of an Intelligent Mobile Gardening Robot Supported by Deep Learning-Based Image Processing

Mutez Rahal(FIRAT UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, COMPUTER ENGINEERING PR.)0009-0000-5819-5072
Abdurrahman Elhac Abdullah(FIRAT UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF MECHATRONICS ENGINEERING, MECHATRONICS ENGINEERING PR.)0009-0004-2298-9590
Hakan Çelik(FIRAT ÜNİVERSİTESİ)0000-0002-2861-3269
Mehmet Kaya(FIRAT UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, COMPUTER ENGINEERING PR.)0000-0003-2995-8282
Pages
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Publication Date
2026-10-12
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AI Summary & Key Takeaways

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

When the results obtained were examined, it was seen that the developed deep learning-based plant detection model achieved an 82% mAP@50 success rate, while the plant classification model achieved a 92.9% mAP@50 success rate.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, the design and control of a mobile gardening robot based on image processing and artificial intelligence were implemented.
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Key Findings & Data:The system, running on Raspberry Pi 4B, can distinguish different plant species and weeds using a YOLOv5-based object detection and classification model; it performs tasks such as cutting weeds, watering useful plants, and moving autonomously while avoiding obstacles.
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Conclusion & Impact:When the results obtained were examined, it was seen that the developed deep learning-based plant detection model achieved an 82% mAP@50 success rate, while the plant classification model achieved a 92.9% mAP@50 success rate.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Smart Agriculture · Deep Learning · Image Processing

Abstract

In this study, the design and control of a mobile gardening robot based on image processing and artificial intelligence were implemented. The system, running on Raspberry Pi 4B, can distinguish different plant species and weeds using a YOLOv5-based object detection and classification model; it performs tasks such as cutting weeds, watering useful plants, and moving autonomously while avoiding obstacles. Experiments conducted in a pot environment demonstrated that the robot can successfully perform plant detection, classification, and maintenance functions. With these features, the developed robot offers a low-cost and sustainable solution for small and medium-scale agricultural businesses. The mobile gardening robot designed within the scope of the study was experimentally tested for image processing-based plant recognition, autonomous plant orientation, plant cutting, and watering operations for different scenarios. When the results obtained were examined, it was seen that the developed deep learning-based plant detection model achieved an 82% mAP@50 success rate, while the plant classification model achieved a 92.9% mAP@50 success rate.

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Cite This Article

Rahal, M., Abdullah, A. E., Çelik, H., Kaya, M. (2026). Design and Control of an Intelligent Mobile Gardening Robot Supported by Deep Learning-Based Image Processing. Journal of Agricultural Sciences, Volume: 32 Issue: 4. https://doi.org/10.15832/ankutbd.1883925

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