Thesis: Smart Agricultural Robot Based on Computer Vision

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dc.contributor.authorHoussam, Mohamed Islam
dc.contributor.authorOmar, Njutapmvoui Mbah Mohamed
dc.contributor.authorCoulibaly, Moussa
dc.date.accessioned2025-03-05T04:50:13Z
dc.date.available2025-03-05T04:50:13Z
dc.date.issued2024-07-08
dc.descriptionSupervised by Dr. Golam Sarowar, Professor, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024en_US
dc.description.abstractThe agricultural sector is using smart agricultural robots, particularly harvesting robots, to increase production, reduce labor costs, and maximize resource use. These robots automate fruit selection, alleviating manpower shortages and lowering manual harvesting costs. A stable power supply system, an ESP32 microcontroller for control, servo motors for precise movement, ultrasonic sensors for obstacle detection, and an ESP32-CAM for picture capture are all essential components. Software technologies such as Fusion 360 help to design the robot's construction, while MATLAB Simulink and Simscape enhance the robot arm's dynamics. Using the YOLO v3 model, the robot detects fruit accurately in real time. These technologies not only improve efficiency and lower costs, but they also encourage sustainable agricultural practices. Future developments aim to enhance autonomy, integrate advanced sensors for better environmental monitoring, and adapt robots to various agricultural settings, ensuring continued innovation in food production technologies.en_US
dc.identifier.citation1. Kamilaris, A.; Prenafeta-Bold´u, F.X. Deep learning in agriculture: A survey. Comput. Elec tron. Agric. 2018, 147, 70–90. [Google Scholar] [CrossRef] [Green Version] 2. . Wang, C.-Y.; Bochkovskiy, A.; Liao, H.-Y.M. Scaled-YOLOv4: Scaling Cross Stage Partial Network. arXiv 2020, arXiv:2011.08036. [Google Scholar] 3. Wojke, N.; Bewley, A.; Paulus, D. Simple Online and Realtime Tracking with a Deep Associ ation Metric. In Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017; pp. 3645–3649. [Google Scholar] 4. Lawal, M.O. Tomato detection based on modified YOLOv3 framework. Sci. Rep. 2021, 11, 1447. [Google Scholar] [CrossRef] 5. .Li, G.; Huang, X.; Ai, J.; Yi, Z.; Xie, W. Lemon-YOLO: An efficient object detection method for lemons in the natural environment. IET Image Process. 2021, 1–12. [Google Scholar] [CrossRef] 6. Itakura, K.; Narita, Y.; Noaki, S.; Hosoi, F. Automatic pear and apple detection by videos using deep learning and a Kalman filter. OSA Contin. 2021, 4, 1688. [Google Scholar] [CrossRef]en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2348
dc.language.isoenen_US
dc.publisherDepartment of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.titleThesis: Smart Agricultural Robot Based on Computer Visionen_US
dc.typeThesisen_US

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