International Journal of Electronic Devices and Networking
2024, Vol. 5, Issue 2, Part A
Development of a Mobile-based expert system for rice disease diagnosis using forward chaining and Real-time data integration
Author(s): Samira Benali and Ahmed Boukhalfa
Abstract: Rice (Oryza sativa L.) is a staple crop feeding over half of the global population, yet its productivity is significantly threatened by diseases such as bacterial blight, blast, sheath blight, and tungro. Traditional diagnostic methods are often time-consuming, reliant on expert intervention, and inaccessible to smallholder farmers. This study aimed to develop and validate a mobile-based expert system for rice disease diagnosis using forward chaining inference algorithms and real-time data integration from IoT-based environmental sensors and GPS-enabled devices. The mobile application was designed with a structured knowledge base derived from scientific literature and expert consultations. Field trials were conducted across ten farms, where farmers and extension workers provided real-time data inputs, including visual symptoms and environmental parameters. Diagnostic accuracy, sensitivity, and specificity of the system were evaluated statistically using confusion matrix analysis and ROC curve analysis. Results revealed an average diagnostic accuracy of 97.01%, with sensitivity at 90.69% and specificity at 85.37%. Farms with better environmental data calibration and trained users demonstrated higher diagnostic precision. Statistical analysis confirmed significant improvement over traditional diagnostic methods, with variability attributed to environmental data noise and user inconsistencies. The study highlights the importance of real-time data acquisition, localized knowledge bases, and user-friendly interfaces in enhancing diagnostic reliability. Future recommendations include integrating AI-based predictive analytics, expanding multilingual support, and implementing farmer training programs for improved data accuracy. This mobile-based expert system represents a scalable, practical solution for disease management, with significant potential to improve crop health, farmer decision-making, and sustainable agriculture practices.
DOI: 10.22271/27084477.2024.v5.i2a.65
Pages: 42-47 | Views: 41 | Downloads: 11
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How to cite this article:
Samira Benali, Ahmed Boukhalfa. Development of a Mobile-based expert system for rice disease diagnosis using forward chaining and Real-time data integration. Int J Electron Devices Networking 2024;5(2):42-47. DOI: 10.22271/27084477.2024.v5.i2a.65
