Adoption and Usability of Mobile AI Expert Systems for Disease Diagno sis among Smallholder Farmers in Nigeria
DOI:
https://doi.org/10.48165/ijaee.2025.1.2.2Keywords:
Artificial intelligence, expert system, mobile application, digital agriculture, smallholder farmers.Abstract
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.
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