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An Intelligent framework for Quality Inspection and Control in Aquaponics, based on Computer-vision, Artificial Intelligence, and Knowledge modeling
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- Author / Creator
- Abbasi, Rabiya
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The burgeoning global population and subsequent food security issues have attracted much attention toward sustainable food production systems. As one of the emerging vertical farming methods, aquaponics promises to be a sustainable alternative to food and environmental problems. Aquaponics in integrated production of fish and hydroponic crops with recirculation of the aquaculture effluent used by the plant as fertilizer. This technique offers high water efficiency, a faster growth rate, and high crop yields. Despite all the advantages offered by this technology, its implementation on a commercial scale is hindered by many technical and economic factors, which can be addressed by integrating smart technologies, automation, and control. This thesis, therefore, aims to support research towards developing solutions for crop quality control and viable commercial aquaponic. For this purpose, the status of digitization in the agriculture industry is first investigated, and potential research gaps in aquaponics are identified. Next, an ontology model is formalized to store relevant knowledge pertaining to different domains of the aquaponic 4.0 system, which can be extracted and used to enable data-driven decisions related to crop quality, facility layout, and system operations. An interactive decision support tool is then developed that uses knowledge from the ontology model to automatically determine the design of grow channels in hydroponic units based on crop characteristics for enhanced crop growth and quality. After that, a cloud-based dashboard is developed for the acquisition of sensors’ data and crop images from the aquaponic facility, which is also linked with the ontology model and other quality assessment tools developed in this research. A crop disease detection system is then developed to detect and identify diseases in leafy green crops, followed by the development of the model that effectively assesses the quality of lettuce crops based on foliage color. Another model is then developed to estimate the crop morphological traits in a particular area and plant site spacing for healthy growth of the crop. Finally, a cloud-based application that acts as a decision support system is designed where all the models are deployed. Implementing this decision support system will assist agriculturalists in various decisions related to growing crops in aquaponics and crop quality control and management, thereby paving the way towards developing a smarter and sustainable food production system.
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- Graduation date
- Spring 2023
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.