Towards Aquaponics 4.0: A Framework for Automation, IoT, and Smart Systems Implementations in Indoor Farming

  • Author / Creator
    Reyes Yanes, Abraham
  • Aquaponics is a farming method that promises to be a good alternative against the food and environmental problem the world is facing. It is a combination between aquaculture (farming of fish) and hydroponics (growing plants without soil), being a technique to grow plants with the aquaculture effluent. This technique claims to have a high water efficiency, is pesticide-free, and reduces the use of fertilizers. All in all this technology is considered green and sustainable. Since the interest in aquaponics is increasing, the major challenge is to provide feasible and reliable solutions at the commercial scale. The concept of precision farming, usually applied in the traditional farming sense, is now being introduced, leading to the need to adopt sensing, smart, and IoT systems for monitoring and control of its automated processes. This thesis aims to support research towards a viable commercial aquaponics solution by first; identifying, listing, and providing an in-depth explanation of each of the parameters sensed in aquaponics, and the smart systems and IoT technologies in the reviewed literature. Secondly, to propose a tool that uses image-processing techniques, deep learning, and regression analysis to estimate the size of the crops as they grow using image segmentation and do a correlation between the size of the crops and their fresh weight for being modelled that will work as a performance metrics. Third, the development of a framework is presented that involves the creation of a wireless sensing module that uses sensing parameters and the connection to a database capable of storing and linking the information to a quality assessment tool. Finally, an application that adopts digital twinning in the growing beds of an aquaponics system for monitoring, in real time, parameters and hence control the aquaponics physical system is developed.

  • Subjects / Keywords
  • Graduation date
    Fall 2020
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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