Abstract:
Tomato production is a critical component of Ethiopia's agricultural sector, particularly in the
Gamo Zone, where smallholder farmers face significant challenges in managing pests and
diseases. Despite the economic importance of tomatoes, farmers often lack access to expert
knowledge and reliable information, which hinders effective management strategies. Previous
research has highlighted the limitations of traditional pest control methods that rely on manual
expertise and insufficient resources, creating a pressing need for innovative solutions. This study
addresses the gap in localized knowledge and seeks to provide farmers with timely and accurate
pest and disease management strategies. This study aimed to design, develop, and evaluate a
Knowledge-Based System (KBS) that facilitates pest control methods and tomato disease
diagnosis in the Gamo Zone. The research employed a Design Science Research approach, the
study utilized mixed methods (interviews, questionnaires, expert consultations) to acquire
knowledge. The developed TPCMDD KBS uniquely integrated explicit rule-based reasoning
(SWI-Prolog) with data-driven machine learning (Python Random Forest) for diagnosis. The
resulting prototype successfully diagnoses 15 key local tomato diseases and 8 pests, providing
tailored control recommendations. User Acceptance Testing (UAT) involving 64 diverse
stakeholders demonstrated high system usability (96%), efficiency (94%), and strong perceived
accuracy (94% positive rating). The integration of machine learning, achieving high objective
accuracy (96% on test data), significantly enhanced the system's diagnostic capabilities.
Participants reported that the KBS significantly enhanced their ability to make informed pest and
disease management decisions. The findings indicate that the hybrid TPCMDD KBS is a valuable
and effective tool for addressing the challenges of pest and disease management in tomato
production in the Gamo Zone. By bridging the gap between expert knowledge and practical
application through this innovative hybrid approach, the KBS contributes to improved
agricultural practices and has the potential to enhance food security in the region
