Designing and Development a Knowledge-Based System for Maize Disease Diagnosis and Treatment in Case of Gamo Zone

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.