Leveraging AI in Distributed Systems for Building Intelligent Expert Systems: Efficient Solutions for Agricultural Challenges in China

This paper introduces a smart agriculture expert system that integrates Internet of Things (IoT) and artificial intelligence (AI) technologies to address common challenges in modern agriculture, such as complex software maintenance and unstructured knowledge of crop diseases. The system comprises key modules: an image recognition tool using EasyDL for pest and disease identification, a knowledge graph-based question-and-answer system and a natural language-supported monitoring and control system. The disease recognition module leverages EasyDL to efficiently train classification models and provide API access for portability. The knowledge graph-based system employs the BiLSTM-CRF model for entity recognition, stores data using RDF triples and utilizes Neo4j for graph storage, enabling efficient problem-solving in real-time. The monitoring and control module converts natural language commands into machine instructions using RapidSCADA, enabling agricultural automation. By integrating IoT and AI, this system presents an innovative and efficient solution for modern smart agriculture, improving operational efficiency and decision-making in agricultural production.