FACTORS THAT HINDER MACHINE LEARNING TECHNOLOGICAL ADOPTION AMONG SMALLHOLDER FARMERS AND POTENTIAL RECOMMENDATIONS FOR CONVENIENT IMPLEMENTATION AND ADOPTION OF THE TECHNOLOGY: A REVIEW OF MALAWI SMALLHOLDER FARMERS

Background: Advances in agricultural technology have a positive contribution to production because they increase agricultural productivity. The current trend of machine learning has the potential to add value in agriculture by providing relevant information to smallholder farmers to farm; however, there is limited adoption and dissemination of this technology in low and middle-income countries. Objectives: This systematic literature review main goal is to ascertain whether smallholder farmers in Malawi are adopting machine learning technology by answering the following questions: (i) What are the barriers to smallholder farmers' adoption of AI technology? (ii) what strategies and policy recommendations can enhance the convenient implementation and sustainable adoption of machine learning technologies among smallholder farmers. Methods: Systematic literature review was conducted and it utilized Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses to analyze the scientific literature associated with factors that hinder adoption and dissemination of this technology in Malawi published from 2010 to 2023. The study assessed articles at google scholar and science direct and focused on the following keywords of Machine learning, hinder, smallholder farmers, technology adoption and Malawi and articles containing all these keywords were further processed to obtain the factors which will need to be addressed for policy recommendations. Results: The study noted that there are inadequate competent personnel in machine learning technology. Poor internet connection which is expensive and inadequate coverage across the country, poor collaboration among stakeholders, lack of implementation framework and lack of awareness of machine learning both among smallholder farmers and extension officers. Conclusions/implications: The study noted there is need for an awareness campaign of machine learning technology among smallholder farmers so that they can capitalize on the technology, designing of relevant policies and regulations that support machine learning, and collaboration among government and non-government organization in training experts in machine learning technology. There is also need for machine learning infrastructure development and gender mainstreaming in dissemination of the technology. There is need to improve internet capacity so that it readily and affordable for smallholder farmers Keywords: Machine learning, hinder, smallholder farmers, technology adoption and Malawi