The research investigates the application of autonomous inspection technologies within the oilseed industry—a sector characterized by the extraction of oil from plant seeds like sunflower and rapeseed. The oilseed industry, with its complex production operations, has historically relied on labor-intensive and error-prone inspection methods that lead to significant financial losses and compromised product quality. The thrust of this research is to develop a systematic approach to assist industry leaders in selecting optimal autonomous inspection technologies for oilseed processing, improving efficiency and reducing human error. Employing an exploratory research approach, this thesis utilizes qualitative methodologies, including case studies and interviews, particularly effective in less researched fields. The research is structured around the double diamond model of design thinking, encompassing four stages: Discover, Define, Develop, and Deliver. First phase in Discover and Define includes a detailed examination of current processes and technologies, followed by the identification and definition of key operational problems. The final phases include developing an approach and validating it with a case study.

A significant portion of the thesis is dedicated to Multi-Criteria Decision Analysis (MCDA), using the Analytic Hierarchy Process (AHP) to evaluate various autonomous inspection technologies. This process systematically ranks technologies based on criteria such as usability, financial viability, and safety standards. The research culminates in a practical validation through a comprehensive case study conducted at Cargill. The case study not only tests the proposed systematic approach but also highlights its effectiveness in identifying optimal technologies. The findings confirm the viability of the proposed selection framework, emphasizing its potential to revolutionize inspection processes in the oilseed industry by integrating autonomous technologies. This thesis contributes to the strategic decision-making process in technological adoption, ensuring that selected technologies align well with industry needs and operational goals.