Masters Degrees (Industrial Engineering)
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Browsing Masters Degrees (Industrial Engineering) by Author "Bekker, Gretchen"
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- ItemDevelopment of an agriculture 4.0 data acquisition technology decision support framework for small-scale farms(Stellenbosch : Stellenbosch University, 2023-03) Bekker, Gretchen; Jooste, Johannes L.; Hummel, Vera; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).ENGLISH ABSTRACT: The Industrial Revolution precedes the development of the Agricultural Revolution. As revolutions unfold, the agricultural industry is experiencing increasing pressure due to population growth, diminishing resources, decreasing yields, and the growing emissions footprint produced by the agricultural sector. SSFs manage 80% of sub-Saharan African and Asian farmland that experiences these elements but does not have the capital capacity to invest in new developments or risk mitigation plans. However, an Agriculture 4.0 characteristic is digitalisation, and this revolution provides technologies and DAQ-sensor technologies, that require little to no capital investment, which can help SSFs manage the elements that cause negative pressure on their farms. The research problem is that Agriculture 4.0 practices have various technological considerations, and there is limited decision support for small-scale farmers (SSF) to gain knowledge and adapt Agriculture 4.0 data acquisition technologies effectively. The thesis adopts the Hutter-Hennink Qualitative Research Cycle as the research methodology and collects data through validation and veri_cation via semi-structured interviews. Developing a decision support framework (DSF) in combination with an analytic hierarchy process (AHP) successfully addresses the research problem. The DSF is developed in combination with AHP, which enables the framework to process user inputs and use SMEs from the agricultural technology industry. Regarding the research problem, Agriculture 4.0 practices are precision agriculture, digital farming and smart farming. All of these practices use technology to build a more sustainable and Agriculture 4.0 industry. Within each practice, there are countless crops, but the thesis focuses on horticulture crops. Land for horticulture crops must be planned and preprepared before planting, and the crops go through multiple growth stages before _nally being harvested. For SSFs, these activities, from ground preparation to harvesting, are the most important. Therefore the DSF is the research product and uses the farm activities (FA) and the key performance indicators (KIPs) as criteria. The DSF consists of _ve phases: (1) Awareness, (2)Criteria, (3)DAQ-sensor technology, (4)Adapt, and (5)Adoption. The _rst phase includes farm characteristics and actions that ought to be done prior to technology adoption. Phase one presents valuable information for the user. Phase two uses FAs and KPIs as criteria to individualise the decision support provided by the framework. The criteria capture users' input for the DSF to identify the users' needs and to align these with suitable DAQ-sensor technologies. The criteria are used similarly in phase 3 by SMEs to indicate the importance of a DAQ-sensor technology concerning each criterion. Phases 2 and 3 perform the AHP and present AHP output calculated by XLSTAT, an additional functionality in MS Excel. In phase 4, the characteristics of the most suitable DAQ-sensor technology according to the AHP output and the capability of each DAQ-sensor technology regarding technologies that enhance compatibility are indicated. The criteria and the technology characteristics both capture the technology considerations from past agricultural frameworks and Agriculture 4.0 practices. Finally, phase 5 presents the DSF's synopsis of phases one to four. The DSF can solve the research problem and has the potential to be developed further.