Abstract:
Abstract— This research paper presents a solution to the
declining yield in potato farming in Kenya, which has
been attributed to a limited supply of quality seeds and
access to expert advice. The study used IBM Watson
Assistant, an AI-based chatbot framework, and GIS to
offer expert advice and link farmers to quality seed
producers. The paper first introduces potato farming in
Kenya and provides a brief history of conversation agents
before delving into the theory behind chatbots, including
their classifications and general architecture. The
methodology section outlines the five significant steps
taken in the research, including data collection,
implementation, testing and training, and evaluation. The
evaluation phase used performance indicators which are
presented in detail. The results demonstrate that this
potato farming chatbot model had a score of 97.7% in
terms of message coverage, a score of 78.4% in terms of
conversation containment, and was 88.05% effective,
users were 60% satisfied with the model and the
likelihood of use of the model in the future was at 80%.
The study concludes that this integrated potato farming
chatbot model is a practical solution for farmers to
improve their yield, and the recommendations made
based on user feedback and expert input could improve
the model further. Overall, the study presents a
promising location-based approach to addressing food
security challenges in Kenya through technology-driven
solutions.
