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Please use this identifier to cite or link to this item: http://hdl.handle.net/10155/1329

Issue Date: 1-Aug-2021
Title: Yield estimation and smart harvesting for precision agriculture using deep learning
Authors: Osman, Youssef
Publisher : University of Ontario Institute of Technology
Degree : Master of Applied Science (MASc)
Department : Electrical and Computer Engineering
Supervisor : Elgazzar, Khalid
Keywords: Precision agriculture
Deep learning
Computer vision
Geospatial data
Agriculture decision support
Abstract: Precision agriculture is one of the fastest growing fields in recent years. In this thesis, we introduce a framework that provides farmers with a yield estimation from videos of crops and provides guided assistance for harvesting across the farm by utilizing geospatial information that is collected during the recording of the crops. We perform yield estimation by using a tracking model, DeepSORT, that can keep track of detected fruits for accurate counting. We modified the original DeepSORT algorithm to work efficiently on different fruits without the need for retraining. The proposed framework also provides assistance for smart harvesting through an optimized approach for container placement across the field. Performance evaluation shows that the proposed method achieves more than 90% accuracy on a real video footage of apple trees collected by a drone from an apple orchard and approximately 94% accuracy for pumpkin counting from an aerial drone footage.
Appears in Collections:Electronic Theses and Dissertations (Public)
Faculty of Engineering and Applied Science - Master Theses

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