Agricultural technology Advancement has a positive contribution in production because it increases agricultural productivity. Machine learning has the potential to add value in agriculture by providing timely appropriate information to smallholder farmers to farm, however there is limited adoption and dissemination of this technology in low- and middle-income countries. To address this problem a literature review was conducted to assess the factors that cause hindering of adoption of this technology and it utilized broader scope methodology and assessed articles at google scholar and keywords of Machine learning, hindering, smallholder farmers, technology adoption and articles containing all these keywords were further processed to obtain the factors which will need to be addressed for policy recommendations. Results: main factors limiting adoption and dissemination of agricultural technology: lack of machine learning technology awareness, lack of relevant policies and regulations which support machine learning, lack of competent experts, insufficient funds allocated to machine learning development and gender imbalance. Recommendations: design policies and regulations which support machine learning technology, train more experts, design awareness campaigns, mainstream both genders in agricultural machine learning and allocation of adequate funds to develop the technology. Conclusion with relevant policy formulation which intervention in agriculture sector to facilitate sustainable implementation of machine learning then agriculture productivity will be increased which will enable to curb food insecurity and poverty alleviation of smallholder farmers.

Keywords: Machine learning, hinder, smallholder farmers, technology adoption