10th International Congress on Information and Communication Technology

Authors - Freedom Khubisa, Oludayo Olugbara
Abstract - This paper presents the development and evaluation of an artificial intelligent (AI) driven web application for detecting maize diseases. The AI application was designed according to the design science methodology to offer accurate and real-time detection of maize diseases through a user-friendly interface. The application used Flask framework and Python programming language, leveraging multiple libraries and Application Programming Interfaces (APIs) to handle aspects such as database, real-time communication, AI models, weather forecast data, and language translation. The application's AI model is a stacked ensemble of cutting-edge deep learning architectures. Technical performance testing was performed using GTmetrix metrics, and the results were remarkable. The WebQual4.0 framework was used to evaluate the application's usability, information quality and service interaction quality. The Cronbach’s alpha (α) reliability measure was applied to assess internal consistency for WebQual4.0, which yielded an acceptable reliability score of 0.6809. The usability analysis showed that users perceived the AI-driven web application as intuitive, with high scores computed for navigation and ease of use. The quality of information was rated positive with users appreciating the reliability and relevance of the maize disease detection results of the AI application. The service interaction indicated potential for enhancement, which is a solicitude also highlighted in qualitative user feed-back that will be considered for future improvement. The study findings generally indicated that our AI application has great potential to improve agricultural practices by providing early maize disease diagnostics and decisive information to aid maize farmers and enhance maize yields.