Design and Implementation of an Expert System for Diagnosing Crop Diseases in Maize, Groundnut, and Millet

Agriculture is a cornerstone of economic growth and food security in Nigeria, particularly in rural states such as Yobe where maize, groundnut, and millet are staple crops. Despite their importance, productivity is severely constrained by crop diseases caused by fungi, bacteria, viruses, and pests. These diseases account for significant yield losses, threatening farmers’ livelihoods and national food security. Traditional diagnostic methods are often slow, costly, and inaccessible to smallholder farmers, who also face challenges of misdiagnosis due to limited technical expertise and inadequate extension services. Consequently, there is a pressing need for affordable, reliable, and accessible diagnostic tools that can empower farmers to make informed decisions. This study addresses these challenges by designing and implementing a rule-based Expert System for diagnosing common diseases in maize, groundnut, and millet. The system integrates a knowledge base of symptoms and diagnostic rules with an inference engine capable of simulating expert reasoning. Using iterative prototyping, the system was developed, tested, and refined to ensure accuracy and usability. By guiding farmers through symptom-based questioning and providing timely recommendations for disease management, the system reduces dependency on scarce agricultural experts and enhances decision-making in rural communities. The significance of this research lies in its contribution to food security, digital agriculture, and the application of Artificial Intelligence in solving real-world agricultural problems. Beyond its practical utility for farmers, the project also demonstrates the academic relevance of expert systems in computer science, showcasing how knowledge-based reasoning can be applied to critical domains. Ultimately, the system provides a localized, accessible, and intelligent solution to crop disease diagnosis, supporting sustainable agricultural productivity and resilience in Nigeria’s farming communities.

agriculture, expert system, crop

1. FAO. (2021). World food and agriculture – Statistical yearbook 2021. Food and Agriculture Organization of the United Nations. [Google Scholar] [Crossref]

2. Alakonya, A., et al. (2018). Crop diseases and post-harvest losses: Implications for farmers’ income and food security. Agricultural Systems. World Bank. (2020). Empowering smallholder farmers: Challenges in accessing extension services. Washington, DC: World Bank Group. [Google Scholar] [Crossref]

3. Oladipo, O., & Ajayi, A. (2019). Development of a rule-based expert system for diagnosing maize leaf diseases using Python. Journal of Computer Science and Its Applications. [Google Scholar] [Crossref]

4. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. [Google Scholar] [Crossref]

5. Patil, J., & Thorat, S. (2016). Artificial intelligence in agriculture: Applications and challenges. International Journal of Computer Applications. [Google Scholar] [Crossref]

6. Giarratano, J., & Riley, G. (2020). Expert systems: Principles and programming (5th ed.). Cengage Learning. [Google Scholar] [Crossref]

7. Keen, P., & Scott Morton, M. (1978). Decision support systems: An organizational perspective. Addison-Wesley. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet