Adoption of Machine Learning by Rural Farms: A Systematic Review

Machine Learning (ML) has seen a major increase as a method to improve operations for businesses and consumers in different industries. It has been highlighted to enhance efficiency for businesses in product creation, product development, marketing, and customer experience. The purpose of this paper was to review worldwide studies investigating ML adoption by rural businesses to determine the level of ML adoption research conducted in the context of rural farms. A systematic literature review incorporating a Template Analysis (T.A.) was conducted to determine the level of research investigating drivers and barriers to ML adoption by rural enterprises. The reviewed studies were selected based on research purpose (investigating the take-up of innovations/technology by rural businesses), year of research (2000–2023), and inclusion of rural businesses in the studies. Additionally, the reviewed studies were analysed based on the year of each study, the geography of the study, the sector, and the size of businesses, including the level of location/rurality of included businesses and the degree of technology/innovation adoption by enterprises. The findings from the study highlight a research problem based on limited research investigating the adoption of ML by rural farms in several regions around the world. Additionally, the findings from the review highlight a lack of clarity on the relationship between the sector and the size of businesses and their adoption of ML. The significance of the highlighted findings is that there is scope for further research investigating the adoption of ML by smaller rural businesses, which may inform their survival and growth and may have wider implications for policymakers and practice. Therefore, encouraging future primary research focusing on ML adoption by rural farms in the regions under-represented in the literature. Additionally, the findings from this paper have policy, practical, and theoretical implications.