Artificial Intelligence in Coffee Roasting: A Systematic Literature Review on Quality Optimization and Process Control
DOI:
https://doi.org/10.34145/ivas.v1i1.3865Keywords:
Artificial Intelligence, Coffee Roasting, Quality Consistency, Sensor Technology, SMEsAbstract
Coffee is an important agribusiness commodity with great potential in the global market. The roasting process, which is crucial in determining coffee quality, is often influenced by temperature and time variability, which can alter the taste, aroma, and color of coffee. In many MSMEs, manual control of this process causes quality instability, affecting competitiveness and profits. Artificial intelligence (AI) technology offers the potential to improve the consistency and quality of coffee products through more precise control. This research is important to assess the application of AI in coffee roasting to improve the quality and efficiency of the process. The objective of this review is to evaluate the application of artificial intelligence in coffee roasting, identify the benefits and challenges faced by farmers and SME actors, and provide recommendations on technologies that can be applied to optimize coffee product quality. This study uses a Systematic Literature Review (SLR) approach. Literature searches were conducted through databases such as Scopus and Web of Science using keywords related to AI and coffee roasting. Inclusion criteria included empirical articles published between 2014 and 2024. Article quality assessment was conducted using the Mixed Methods Appraisal Tool (MMAT), and data were extracted using a thematic analysis approach. A total of 25 articles were included in the analysis. The results showed that the application of AI in coffee roasting mainly focused on temperature and time control to improve consistency. Technologies such as real-time monitoring, predictive models, and sensory systems proved to be effective in reducing process variability. Despite significant benefits, challenges included high initial costs and the need for operator training. The application of AI in coffee roasting can improve quality and efficiency, but its adoption at the SME level is limited by costs and infrastructure constraints. Further research is needed to address the practical challenges in implementing this technology.
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