Abstract:
In major parts of Ethiopia, maize is one of the most important strategic cereal crops produced
for human use. It is also an essential source of food for animals. Nevertheless, In the Gamo
Zone, known for its agricultural potential, faces challenges in maize production due to
diseases, requiring competent professionals to diagnose and provide timely treatment and
prevention methods, despite its significance. To address these challenges, this study aims to
develop a knowledge-based system for maize disease diagnosis and treatment specifically
tailored for the Gamo Zone. This problem requires competent and educated professionals to
diagnose the diseases and explain treatment and prevention methods in the early stages of
infestation. In the development of the knowledge-based system, design science research
methodology was followed to develop a prototype system of explanation facility. The
purposive sampling method was used to select domain experts. The required knowledge was
gathered using both structured interviews and document analysis techniques. The acquired
knowledge was represented using the if-then rule. A decision tree modeling tool was used to
model the represented rules. Implementation was developed using a swi-prolog programming
tool. The developed prototype was tested and evaluated using the user acceptance testing
method. The user acceptance evaluation was 88% by the domain experts. The proposed
knowledge-based system would recommend for future research to incorporate high-quality
images that depict the symptoms and show the degree of damage to the infected part of the
maize crop and it is better to add an update feature to the system.
