Industry Overview - The report focuses on the development and application of artificial intelligence (AI) in agriculture, particularly in the context of smart agriculture, which integrates modern information technologies such as IoT, big data, and cloud computing to enhance agricultural productivity and efficiency [6][18] - Smart agriculture is considered the core of China's agricultural 4.0, following traditional agriculture (1.0), mechanized agriculture (2.0), and biological agriculture (3.0) [18] - The application of AI in agriculture spans pre-production, production, and post-production stages, with technologies such as expert systems, agricultural robots, and computer vision significantly improving labor efficiency and reducing costs [8] Key Technologies in Smart Agriculture - **Agricultural Data Mining**: The report highlights the importance of data mining in agriculture, which involves extracting valuable information from large-scale, heterogeneous agricultural data to support precision farming, resource optimization, and environmental monitoring [30][31] - **Agricultural Semantic Analysis**: Semantic analysis is used to model agricultural knowledge, enabling better understanding and utilization of unstructured data such as text, images, and videos [47][48] - **Pest and Disease Image Recognition**: AI-based image recognition technologies are being developed to automatically identify and monitor agricultural pests and diseases, improving the accuracy and efficiency of pest control [56][57] Applications of AI in Agriculture - **Expert Systems**: AI-powered expert systems provide farmers with decision-making support for crop management, disease diagnosis, and irrigation, significantly enhancing agricultural productivity [8][19] - **Agricultural Robots**: Robots are increasingly used in tasks such as fruit picking, grafting, and weeding, reducing labor costs and improving efficiency [16][74] - **Precision Farming**: Technologies such as automatic navigation for tractors, targeted pesticide application, and smart irrigation systems are being deployed to optimize resource use and increase crop yields [94][101] Challenges and Future Prospects - **Technological Gaps**: Despite advancements, China's smart agriculture still lags behind developed countries in terms of technological maturity, with challenges such as high initial investment costs, low mechanization levels, and insufficient technical expertise [21][22] - **Future Development**: The report suggests that smart agriculture should focus on integrating 3S technologies (GIS, GPS, RS), developing intelligent equipment, and promoting the application of AI in large-scale farms and agricultural demonstration zones [27][29] Case Studies and Real-world Applications - **Crop Production Decision Systems**: These systems use AI to optimize crop management, including planting, fertilization, and irrigation, based on real-time data and predictive models [40][41] - **Aquaculture Management Systems**: AI is applied in aquaculture to monitor water quality, feed management, and disease prevention, improving the efficiency and sustainability of fish farming [49][51] - **Agricultural Drones**: Drones are used for tasks such as crop monitoring, soil analysis, and pesticide spraying, providing a cost-effective solution for large-scale farming [117][119]