Adoption and Usability of Mobile AI Expert Systems for Disease Diagno sis among Smallholder Farmers in Nigeria (International Journal of Agricultural Extension and Education, 2025)
The production of rice in Nigeria is hindered by recurring disease outbreaks and  limited access to timely agronomic advisory services. In response to this challenge,  a mobile artificial intelligence (AI) expert system, RiceAdvisor, has been developed  to assist smallholder farmers in diagnosing rice diseases. This study evaluated the  adoption and usability of RiceAdvisor through the lens of the Unified Theory of  Acceptance and Use of Technology (UTAUT) and methods of usability evaluation. A  quasi-experimental design was employed, consisting of a survey of 300 smallholder  rice farmers and usability testing with 120 participants in three of Nigeria’s most  economically important rice-producing states. Respondents displayed positive  perceptions along the UTAUT constructs, particularly in the areas of Performance  Expectancy (x = 4.07, SD = 0.63), Effort Expectancy (x = 3.92, SD = 0.70), Social  Influence (x = 3.71, SD = 0.74), Facilitating Conditions (x = 3.62, SD = 0.79), and  Behavioural Intention (x = 3.95, SD = 0.66). Multiple regression results indicated the  significance of Performance Expectancy (β = 0.382, p < 0.001), Effort Expectancy (β =  0.297, p < 0.001), Social Influence (β = 0.086, p = 0.016), and Facilitating Conditions  (β = 0.321, p < 0.001) as predictors of Behavioural Intention which also in turn was a  significant predictor of Use Behaviour (β = 0.375, p < 0.001). The regression analysis  showed that the model explained 46% of the variability in intention (R² = 0.46) and  31% of the variability in use behaviour (R² = 0.31). Usability testing showed an  overall successful task completion of 87%, with some variation based on educational  attainment and experience with smartphones. Major obstacles to completion  included connectivity limitations, restricted access to devices, and low digital  literacy levels. The results indicated that if infrastructure and usability are given the  utmost attention, mobile AI expert systems like RiceAdvisor can greatly improve the  diagnostic capability of diseases, assist in the value chain decision-making processes,  and extend advisory services.
Keywords: Artificial intelligence, expert system, mobile application, digital agriculture, smallholder farmers.