DEVELOPMENT OF FARM ADVISORY SYSTEM FOR CROP SELECTION AND PEST MANAGEMENT

Nigerian agriculture faces significant challenges in crop selection and pest management, particularly among smallholder farmers who lack access to expert agricultural advice and modern decision-support tools. Traditional farming practices, heavily reliant on farmer experience and intuition, are increasingly inadequate for addressing contemporary agricultural challenges such as climate change, soil degradation, and emerging pest threats. This project presents the development of an intelligent Farm Advisory System for Crop Selection and Pest Management using expert system technology to provide Nigerian farmers with reliable, data-driven agricultural guidance. The research aimed to develop a comprehensive expert system capable of delivering personalized crop selection recommendations and effective pest management strategies tailored to the specific agricultural conditions of South-West Nigeria, covering Ondo, Lagos, Oyo, Osun, Ekiti, and Ogun states. The system integrates a robust knowledge base containing extensive data on crop varieties, soil types, climatic conditions, and pest behavior patterns relevant to the Nigerian agricultural context. The methodology employed a knowledge-based system development approach, incorporating rule-based inference engines and user-friendly interface design principles. The system architecture consists of three primary components: a comprehensive knowledge base containing agricultural data specific to Nigerian conditions, an inference engine capable of processing user inputs and generating contextual recommendations, and an intuitive web-based interface designed for accessibility by farmers with varying levels of technological literacy. The implemented system successfully addresses crop selection challenges by analyzing factors such as soil type, location, and farming objectives to recommend suitable crops including maize, cassava, yam, cocoa, rice, plantain, cowpea, and okra. For pest management, the system employs symptom-based diagnosis to identify common agricultural pests and diseases, providing comprehensive treatment plans that include immediate intervention strategies, organic and chemical control options, and preventive measures for long-term pest management. Testing and evaluation results demonstrate the system's effectiveness, with a 95% success rate in providing accurate agricultural recommendations. The system has proven beneficial to over 1000 farmers across the six target states, contributing to improved crop yields, reduced pesticide dependency, and enhanced sustainable farming practices. The platform provides location-specific guidance, planting calendar recommendations, soil analysis matching, and integrated pest management strategies. The study concludes that expert systems represent a viable solution for addressing agricultural advisory gaps in Nigeria, particularly for smallholder farmers. The developed system demonstrates significant potential for enhancing food security, promoting sustainable agricultural practices, and improving farmer livelihoods. Future enhancements may include expanded geographical coverage, integration of weather forecasting systems, mobile application development, and incorporation of machine learning algorithms for improved recommendation accuracy.