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Artificial intelligence (AI) can reduce soil compaction, cost, labor, and resource savings in agriculture. Despite visible benefits, AI and many other related technologies are not adopted by farmers. Although existing studies examine the socioeconomic and farmer characteristics that determine adoption of AI majority ignore the perceptions of AI despite the fast development of AI technologies particularly in agriculture. This study purposely sampled and interviewed 100 smallholder farmers in Kiambu County, Kenya, to identify their adoption and perceptions toward AI. The study adopted principal component analysis (PCA) to analyze the data. The findings show that the farmers were aware of AI (62%). Overall, farmers perceive AI and digital technologies positively, citing benefits such as they facilitate easy access to information, loans, and markets; enhance output; revolutionize agriculture/agribusinesses; and increase household income among other benefits. However, some respondents strongly disagreed with the statements that AI has ambiguous data input requirements (17%), has low adaptability to peculiar business situations (19%), and does not consider the challenges women farmers experience (21%). Additionally, the PCA technique grouped the 20 variables into eight categories: increased returns, access to productive resources, information access and decision-making, AI technology and women’s access, functionality, specific requirements, availability, and lack of knowledge factors. The determinants of AI adoption were marital status, land size, and education level. These findings contribute to the existing literature by shedding light on how preexisting sex -based factors, such as land ownership and size, marital status, and education level, shape AI adoption in Agriculture. These findings offer helpful suggestions for policymakers: not to treat AI technology as a magic bullet, but to implement measures that would increase the use of AI in agriculture, reducing the sex, economic, and technological gaps in Agriculture.
Majiwa et al. (Wed,) studied this question.